System for determining traffic metrics of a road network

ABSTRACT

Disclosed are systems and methods relating to providing intersection metrics based on road network data and telematic data.

RELATED APPLICATIONS

This application is a Continuation-in-part of U.S. application Ser. No.16/877,936, filed May 19, 2020, titled “Method For Defining RoadNetworks”, which is herein incorporated by reference in its entirety,and which claims the benefit under 35 U.S.C. § 119(e) to U.S.application Ser. No. 16/535,527, filed Aug. 8, 2019, titled “IntelligentTelematics System For Defining Vehicle Ways”, which is also hereinincorporated by reference in its entirety, and which claims benefitunder 35 U.S.C. § 119(e) to U.S. Provisional Application Ser. No.62/829,539 filed Apr. 4, 2019, titled “Intelligent Telematics System ForProviding Traffic Metrics”, which is herein incorporated by reference inits entirety. This application also claims the benefit under 35 U.S.C. §119(e) to U.S. Provisional Application Ser. No. 63/048,268, filed Jul.6, 2020, titled “Intersection Metrics”, which is also hereinincorporated by reference in its entirety.

BACKGROUND

Telematics systems have been employed by fleet owners to monitor use andperformance of vehicles in the fleet. Analysis of vehicle data collectedby such telematics systems can be useful for extracting meaningful dataregarding behaviour of a group of vehicles.

SUMMARY

According to a first broad aspect there is a method for providingtraffic metrics for a first intersection of a road network comprising:providing first road network data indicating a first plurality of roadnetwork subzones defining an area occupied by the road network, the roadnetwork including a plurality of intersections; processing first roadnetwork data for labelling each road network subzone as one of a coresubzone and non-core subzone for forming a first plurality of coresubzones and a first plurality of non-core subzones, respectively, andstoring an indication thereof in the first road network data; mappingeach of the first plurality of core subzones to an intersection of theplurality of intersections of the road network and storing an indicationthereof in the first road network data, the first plurality of coresubzones including a plurality; forming a plurality of subsets of coresubzones of the first plurality of core subzones, each thereof defininga geographic area occupied by an intersection core of an intersection ofthe plurality of intersections of the road network; processing firstroad network data for mapping each non-core subzone of the firstplurality of non-core subzones to at least an intersection of theplurality of intersections of the road network; forming second roadnetwork data including the first road network data and an indication ofthe at least an intersection of the plurality of intersections of theroad network each non-core subzone of the first plurality of non-coresubzones is mapped forming trip metadata dependent on second roadnetwork data and third vehicle data corresponding to the first pluralityof road network subzones; and processing trip metadata associated withthe first intersection for forming traffic metrics data indicative ofintersection metrics for the first intersection.

In an embodiment there is a method wherein processing first road networkdata for mapping each non-core subzone of the first plurality ofnon-core subzones to at least an intersection of the plurality ofintersections of the road network includes, for each road networksubzone of the first plurality of road network subzones, processing roadnetwork data for forming representative point data indicatingrepresentative points representing the location of the road networksubzone, for each intersection of the plurality of intersections,processing representative point data of a corresponding intersectioncore and representative point data of the first plurality of non-coresubzones for clustering corresponding representative points into groups,and for each representative point of a non-core subzone of the firstplurality of non-core subzones grouped in a same group as representativepoints of an intersection core, mapping the non-core subzone to thecorresponding intersection of the intersection core.

In an embodiment there is a method wherein processing representativepoint data of a corresponding intersection core and representative pointdata of the first plurality of non-core subzones for clusteringcorresponding representative points into groups includes, for at least aroad network subzone, processing road network data for determining acentre point of the road network subzone.

In an embodiment there is a method wherein processing representativepoint data of a corresponding intersection core and representative pointdata of the first plurality of non-core subzones for clusteringcorresponding representative points into groups includes clustering thecorresponding representative points into groups using a spatialclustering algorithm.

In an embodiment there is a method wherein the spatial clusteringalgorithm includes density-based spatial clustering of applications withnoise spatial clustering algorithm.

In an embodiment there is a method wherein forming trip metadatadependent on second road network data and third vehicle datacorresponding to the first plurality of road network subzones includes,for a plurality of vehicles corresponding to the third vehicle data,selecting at least a first subset of temporally consecutive thirdvehicle data instances indicating the vehicle transitions from a firstundrivable state to a second drivable state to a third undrivable statefor forming journey data; processing journey data and road network datafor mapping each instance of journey data to a road network subzone ofthe first plurality of road network subzones based on the journey datainstance corresponding to a road network subzone of the first pluralityof road network subzones and storing an indication of the location ofthe road network subzone, the label of the road network subzone, andintersection mapping of the road network subzone in the journey data;selecting subsets of data instances for forming trip data indicative ofvehicle trips and mapping trip data to an intersection of the pluralityof intersections of the road network; and processing each trip datainstance of the trip data forming trip metadata.

In an embodiment there is a method wherein for a plurality of vehiclescorresponding to the third vehicle data, selecting at least a firstsubset of temporally consecutive third vehicle data instances indicatingthe vehicle transitions from a first undrivable state to a seconddrivable state to a third undrivable state for forming journey data,includes, for at least a vehicle, selecting at least a sequence of thirdvehicle data instances including a third vehicle data instanceindicating ignition status of the vehicle is OFF, immediately followedby a third vehicle data instance indicating the ignition status of thevehicle is ON and the vehicle has a speed greater than 0 km/h,immediately followed by one or more third vehicle data instancesindicating the vehicle is ON, immediately followed by a third vehicledata instance indicating ignition status of the vehicle is OFF.

In an embodiment there is a method wherein selecting subsets of datainstances for forming trip data indicative of vehicle trips includes,selecting at least a first sequence of journey data instances fromjourney data for forming trip data, the at least a first sequence ofjourney data instances including at least one journey data instancecorresponding to a core subzone that is mapped to a first intersection,and immediately followed by a journey data instance mapped to a secondintersection, wherein the second intersection and the first intersectionare not the same intersection and mapping each trip data instance to thefirst intersection and storing an indication of the mapping therein.

In an embodiment there is a method wherein selecting subsets of journeydata instances for forming trip data indicative of vehicle tripsincludes, selecting at least a first sequence of journey data instancesfrom journey data including at least one journey data instancecorresponding to a core subzone that is mapped to a first intersection,and immediately followed by a journey data instance mapped to a secondintersection, wherein the second intersection and the first intersectionare not the same intersection and selecting a second sequence of journeydata instances including at least a journey data instance correspondingto a non-core subzone mapped to the first intersection immediatelypreceding the at least a first sequence of journey data instances andforming trip data based on the first sequence of journey data instancesand the second sequence of journey data instances, mapping each tripdata instance to the first intersection and storing an indication of themapping therein.

In an embodiment there is a method wherein forming trip metadataincludes forming trip metadata selected from a group of: Hardwarelddata, VIN data, Make data, Model data, VehicleYear data, WeightClassdata, VehicleType data, Vocation data, TripID data, IntersectionId data,TimezoneName data, EventStartTimeUTC data, EventEndTimeUTC data,EventStartTimeLocal data, EventEndTimeLocal data, StartingLocation data,EntryCardinal data, ExitCardinal data, StreetNameEntry data,StreetNameExit data, SignalUsed data, TurnSignals data, TravelTime data,TravelDistance data, Travel Speed data, RunningTime data, RunningSpeeddata, MaxSpeed data, MinSpeed data, StopTimeTotal data, NumberOfStopsdata, TimeFromFirstStop data, DistanceFromFirstStop data, CoreDistancedata, MaxAcceleration data, and MinAcceleration data.

In an embodiment there is a method wherein forming EntryCardinal datacomprises determining a bearing between position data corresponding tothe first instance of corresponding trip data located in the core andthe immediately preceding the instance of the corresponding journey dataand creating EntryCardinal data indicative of the bearing.

In an embodiment there is a method wherein forming ExitCardinal datacomprises determining a bearing between position data corresponding tothe last instance of corresponding trip data located in the core and theimmediately following instance of the corresponding journey data andcreating ExitCardinal data indicative of the bearing.

In an embodiment there is a method wherein processing trip metadataassociated with the first intersection for providing traffic metricsdata indicative of intersection metrics for the first intersectionfurther includes selecting a subset of trip metadata associated with thefirst intersection for forming filtered trip metadata and processingfiltered trip metadata for providing traffic metrics for the firstintersection. In an embodiment there is a method wherein selecting asubset of trip metadata for forming filtered trip metadata includesselecting a subset of trip metadata dependent on filter data.

In an embodiment there is a method wherein filter data is selected froma group of: HardwareId data, VIN data, Make data, Model data,VehicleYear data, WeightClass data, VehicleType data, Vocation data,IntersectionId data, TimezoneName data, EventStartTimeUTC data,EventEndTimeUTC data, EventStartTimeLocal data, EventEndTimeLocal data,StartingLocation data, EntryCardinal data, ExitCardinal data,StreetNameEntry data, StreetNameExit data, SignalUsed data, TurnSignalsdata, TravelTime data, TravelDistance data, TravelSpeed data,RunningTime data, RunningSpeed data, MaxSpeed data, MinSpeed data,StopTimeTotal data, NumberOfStops data, TimeFromFirstStop data,DistanceFromFirstStop data, CoreDistance data, MaxAcceleration data, andMinAcceleration data.

In an embodiment there is a method wherein filter data indicates one ormore days of the week.

In an embodiment there is a method wherein filter data includes filterdata provided by a user.

In an embodiment there is a method wherein forming intersection metricsdata includes forming intersection metrics data selected from a groupof: Percentage Stopping, AvgTravelSpeed, AvgRunningSpeed,AvgTotalTimeStopped, AvgTotalTimeStoppedNoZero, AvgTravelTime,AvgTimeFromFirstStop, AvgNumberOfStops, AvgNumberOfStopsNoZero,AvgDistanceFromIntersectionToFirstStop, PercentOfVolumeByVehicleClass,and NumberOfTrips.

In an embodiment there is a method wherein forming trip metadataStreetNameEntry data, for at least an intersection of the plurality ofintersections, includes receiving data indicative of roads and locationthereof within the area occupied by core subzones of the correspondingintersection core; mapping each core subzone of the intersection core tothe nearest road; and generating StreetNameEntry data indicatingassociated with the first core subzone of the trip.

In an embodiment there is a method wherein forming trip metadataStreetNameEntry data, for at least an intersection of the plurality ofintersections, includes receiving data indicative of roads and locationthereof within the area occupied by core subzones of the correspondingintersection core; mapping each core subzone of the intersection core tothe nearest road; and generating StreetNameEntry data indicating thename of the road associated with the last core subzone of the trip.

BRIEF DESCRIPTION OF DRAWINGS

Embodiments of the invention are now described by way of non-limitingexample and are illustrated in the following figures in which likereference numbers indicate like features, and wherein:

FIG. 1A is a simplified diagram of an exemplary network configurationwith which some embodiments may operate.

FIG. 1B is a simplified diagram of the exemplary network configurationof FIG. 1A illustrating communication paths.

FIG. 1C is a simplified diagram of another exemplary networkconfiguration with which some embodiments may operate illustratingcommunication paths.

FIG. 2 is a simplified block diagram of an exemplary telematics system.

FIG. 3A is a simplified block diagram of an exemplary traffic analyticssystem according to an embodiment.

FIG. 3B is a simplified block diagram of another exemplary trafficanalytics system comprising a data management system according to anembodiment.

FIG. 4A is a conceptual diagram of a database of a traffic analyticssystem according to an embodiment.

FIG. 4B is a conceptual diagram of a dataset of the database of FIG. 4A.

FIG. 4C is a conceptual diagram of a vehicle's path within a geographicarea.

FIG. 5A is a simplified diagram illustrating an exemplary intelligenttelematics system according to embodiments.

FIG. 5B is a simplified diagram illustrating another exemplaryintelligent telematics system according to embodiments.

FIG. 6Ai is a simplified diagram of an exemplary type of vehicle way.

FIG. 6Aii is a simplified diagram of an exemplary type of vehicle way.

FIG. 6Aiii is a simplified diagram of an exemplary type of vehicle way.

FIG. 6Aiv is a simplified diagram of an exemplary type of vehicle way.

FIG. 6Av is a simplified diagram of an exemplary type of vehicle way.

FIG. 6Avi is a simplified diagram of an exemplary type of vehicle way.

FIG. 6Bi is a conceptual diagram of a specific and non-limiting exampleof a zone encompassing the vehicle way of FIG. 6Ai.

FIG. 6Bii is a conceptual diagram of a specific and non-limiting exampleof a zone encompassing the vehicle way of FIG. 6Aii.

FIG. 6Biii is a conceptual diagram of a specific and non-limitingexample of a zone encompassing the vehicle way of FIG. 6Aiii.

FIG. 6Biv is a conceptual diagram of a specific and non-limiting exampleof a zone encompassing the vehicle way of FIG. 6Aiv.

FIG. 6Bv is a conceptual diagram of a specific and non-limiting exampleof a zone encompassing the vehicle way of FIG. 6Av.

FIG. 6Bvi is a conceptual diagram of a specific and non-limiting exampleof a zone encompassing the vehicle way of FIG. 6Avi.

FIG. 6C is a conceptual diagram of elements of a vehicle way.

FIG. 7 is a flow diagram of a process for defining a classifier for usein defining a vehicle way according to some embodiments.

FIG. 8 is a simplified diagram of an area comprising a plurality ofsample intersections.

FIG. 9 is a simplified diagram of a plurality of exemplary zones imposedon the plurality of the sample intersections of FIG. 8.

FIG. 10A is a simplified diagram of a plurality of exemplary referenceareas of a same dimension defined for the sample intersections FIG. 8.

FIG. 10B is a simplified conceptual diagram of a plurality of exemplaryzones imposed on sample vehicle ways.

FIG. 11A is a table defining the relationship between Geohash stringlength and approximate Geohash cell dimensions.

FIG. 11B is a simplified functional block diagram of an exemplaryGeohash encode function.

FIG. 11C is a simplified functional block diagram of an exemplaryGeohash decode function.

FIG. 11D is a simplified functional block diagram of an exemplaryGeohash bounds function.

FIG. 11E is a simplified functional block diagram of an exemplaryGeohash neighbours function

FIG. 11F is a simplified conceptual diagram of a Geohash cell.

FIG. 11G is a conceptual diagram of a Geohash cell and 8 nearestneighbours of the Geohash cell.

FIG. 12A is a flow diagram of an exemplary process for subdividing areference area into a grid of Geohash cells.

FIG. 12B is a simplified diagram of a reference area, a reference pointand a Geohash cell to which the reference point was mapped.

FIG. 12C is a simplified conceptual diagram of a centre cell and its 8closest neighbouring cells.

FIG. 12D is a simplified conceptual diagram of a reference areacompletely subdivided into a grid of contiguous Geohash cells.

FIG. 13A is a simplified conceptual diagram of exemplary first subzonedata.

FIG. 13B is a simplified conceptual diagram of other exemplary firstsubzone data.

FIG. 14A is a simplified conceptual diagram of two zones comprisingcentre subzones and a plurality of common subzones located within anoverlapping portion thereof.

FIG. 14B is an enlarged view of a portion of zones comprising anoverlapping portion.

FIG. 14C is a simplified conceptual diagram of redefined zones.

FIG. 15A is a simplified conceptual diagram of a zone comprisingvehicle-position data points representing positions of vehicles therein.

FIG. 15B is an enlarged view of a portion of an area and the simplifiedconceptual diagram of FIG. 15A imposed thereon.

FIG. 16A an enlarged view of a portion of an area comprising a sampleintersection and paths of vehicles that have traversed therethrough.

FIG. 16B is a conceptual diagram of a zone and vehicle position-datapoints representing position data of raw vehicle data instances thatcorrespond to a position along vehicle paths.

FIG. 16C illustrates vehicle position-data points corresponding topositions of vehicles in an area.

FIG. 16D illustrates vehicle-position data points corresponding tointerpolated data instances.

FIG. 16E is a conceptual diagram of a zone comprising a plurality ofsubzones illustrating vehicle-position data points corresponding to rawvehicle data and interpolated vehicle data.

FIG. 16F illustrates vehicle-position data points inside a zonecorresponding to interpolated data.

FIG. 17 is a simplified diagram of an exemplary implementation of atraffic zone encompassing zones.

FIG. 18A is a simplified block diagram of a process for obtainingvehicle data for generating features.

FIG. 18B is a simplified block diagram of another process for obtainingvehicle data for generating features.

FIG. 19A is a conceptual block diagram of a feature extraction functionfor generating features from vehicle data.

FIG. 19B is an exemplary table representing data comprising each subzoneID and a plurality of features for each Geohash.

FIG. 20A is a conceptual diagram of a portion of a zone.

FIG. 20B enlarged view of a subzone.

FIG. 20C is a simplified functional block diagram of a function that maybe implemented for generating at least one feature.

FIG. 20D is a table representing an example subset of vehicle datainstances corresponding to a position within a subzone.

FIG. 20E is a table of exemplary features and feature values based onthe subset of vehicle data instances of FIG. 20D.

FIG. 21 is a simplified diagram of a subzone and vehicle-position datapoints.

FIG. 22A is a simplified diagram of a path of a vehicle that hastraversed a subzone.

FIG. 22B is a table representing a subset of vehicle data.

FIG. 23 is a simplified diagram of a portion of a zone.

FIG. 24A is a simplified conceptual diagram of a portion of a zoneincluding a subzone having 8 adjacent subzones.

FIG. 24B is a simplified conceptual diagram of a portion of a zoneincluding a subzone having 4 adjacent subzones.

FIG. 25A is a conceptual diagram of a portion of a sample vehicle wayand a zone imposed thereon.

FIG. 25B is a table representing training data including subzone ID, aplurality of features for each associated subzone, and a class label.

FIG. 25C is a simplified high-level flow diagram of an exemplary processfor using a machine learning technique to define a classifier.

FIG. 26A is a flow diagram of a process for defining a road networkaccording to an embodiment.

FIG. 26B is a conceptual diagram of a database comprising secondhistorical vehicle data.

FIG. 26C is a conceptual diagram of a dataset comprising raw vehicledata instances indicative of vehicle operation information collected bya monitoring device at different points in time.ope

FIG. 27 is a simplified diagram of an exemplary road network areadefined by latitude-longitude pairs.

FIG. 28A is a conceptual diagram of a road network zone comprising aplurality of Geohashes.

FIG. 28B is exemplary second subzone data.

FIG. 28C is another exemplary second subzone data.

FIG. 29 is a simplified diagram of a road network zone, exemplary pathstaken by three vehicles that have traversed therethrough and vehicleposition-data points corresponding to the paths.

FIG. 30 is a simplified diagram of a road network zone, exemplary pathstaken by three vehicles that have traversed therethrough, vehicleposition-data points corresponding to the paths and interpolated vehicleposition-data points.

FIG. 31 is a simplified diagram of a second traffic zone, encompassing aroad network zone, exemplary paths taken by three vehicles that havetraversed therethrough and vehicle position-data points corresponding tothe paths.

FIG. 32 is a simplified diagram of a second traffic zone, encompassing aroad network zone, exemplary paths taken by three vehicles that havetraversed therethrough, vehicle position-data points corresponding tothe paths and interpolated vehicle position-data points.

FIG. 33 is a conceptual diagram of a second subzone comprisingvehicle-position data points indicative of a position of one or morevehicles that have entered the second subzone at one point in time.

FIG. 34A is a simplified functional block diagram of an exemplaryprocess for generating second subzone-related features for a secondsubzone.

FIG. 34B is an exemplary first subset of second vehicle datacorresponding to a position within a second subzone.

FIG. 34C is an exemplary second subzone-related features and featurevalues based on the first subset of second vehicle data instances.

FIG. 35 is a simplified diagram of a second subzone and avehicle-position data point representing a vehicle position at T1according to a first subset of second vehicle data.

FIG. 36A is a simplified diagram of a path of a vehicle that hastraversed a second subzone.

FIG. 36B is an exemplary third subset of second vehicle datacorresponding to vehicle-position data points at times T11-T24.

FIG. 37A is a conceptual diagram of a portion of a road network zone.

FIG. 37B is another conceptual diagram of a portion of a road networkzone.

FIG. 38A is a conceptual diagram of exemplary features generated for asecond subzone.

FIG. 38B is a conceptual diagram of exemplary unlabelled data.

FIG. 39A is a conceptual block diagram of a classifier receivingunlabelled data and providing classification data.

FIG. 39B is a conceptual diagram of exemplary classification dataillustrating each second subzone has been assigned a label, i.e., 1 or0, indicating whether the area corresponding to a second subzone is aportion of a vehicle way, or not a portion of a vehicle way.

FIG. 40A is a diagram of a road network zone illustrating secondsubzones labelled as a portion of a vehicle way shaded grey.

FIG. 40B is a conceptual diagram of a GeoJSON file indicatinggeographical boundaries of a road network.

FIG. 40C is a conceptual diagram of road network indicative of a GeoJSONfile.

FIG. 41 is a flow diagram of a process for relabelling second subzonesof a road network that have been incorrectly classified by a classifier.

FIG. 42A is a conceptual diagram of a simplified road network zone.

FIG. 42B is exemplary classification data indicating a second subzone IDfor each geohash in a road network zone and classification thereof.

FIG. 42C is exemplary neighbour data indicating neighbours of eachGeohash in a road network zone.

FIG. 42D is another conceptual diagram of a simplified road network zoneillustrating a first neighbour count for each second subzone of a roadnetwork zone within the respective second subzone.

FIG. 42E is another conceptual diagram of a simplified road network zoneillustrating a neighbour sum for each second subzone of a road networkzone within the respective second subzone.

FIG. 42F is a conceptual diagram of exemplary classification data.

FIG. 42G is another conceptual diagram of a simplified road network zoneillustrating a second neighbour count for each second subzone of a roadnetwork zone within the respective second subzone.

FIG. 42H is a conceptual diagram of other exemplary classification data.

FIG. 42I is another conceptual diagram of a simplified road network zoneillustrating a third neighbour count for each second subzone of a roadnetwork zone within the respective second subzone.

FIG. 42J is a conceptual diagram of other exemplary classification data.

FIG. 43 is a conceptual diagram of exemplary classification data.

FIG. 44A is a conceptual diagram of a road network zone illustrating aroad network including second subzones shaded according toclassification data.

FIG. 44B is a conceptual diagram of a GeoJSON file indicatinggeographical boundaries of a road network based on classification data.

FIG. 44C is a conceptual diagram of a road network indicative of aGeoJSON file.

FIG. 44D is a conceptual diagram of exemplary road network data createdby a traffic analytics system;

FIG. 45A is a conceptual diagram of an exemplary road network;

FIG. 45B is a conceptual diagram of a first plurality of road networksubzones representing a geographical area occupied by a road network;

FIG. 45C is exemplary road network data defining a road network;

FIG. 46 is a flow diagram of a process for providing traffic metrics ofan intersection according to an embodiment;

FIG. 47A is a flow diagram of a process for core subzones definingintersection cores and identifying non-core subzones;

FIG. 47B is a conceptual diagram of traffic control equipment-positiondata points representing locations of traffic control equipment locatedwithin a geographic area including a road network;

FIG. 47C is a conceptual diagram of traffic control equipment clusteredinto groups;

FIG. 47D is another conceptual diagram of traffic control equipmentclustered into groups and an intersection reference point indicating acentroid thereof;

FIG. 47E is a conceptual diagram of the first plurality of road networksubzones and intersection reference areas and intersection referencepoints 4 superposed thereon;

FIG. 47F is a flow diagram of an exemplary process for determiningwhether a road network subzones overlaps an intersection reference areafor labelling the road network subzone as one of a core and non-coresubzone;

FIG. 47G is a simple block diagram of an exemplary Geohash decodefunction for resolving a Geohash string to a centre location of thecorresponding Geohash;

FIG. 48 is another conceptual diagram of the first plurality of roadnetwork subzones including a first plurality of core subzones and thefirst plurality of non-core subzones;

FIG. 49A is a conceptual diagram of exemplary modified first roadnetwork data;

FIG. 49B is another conceptual diagram of exemplary modified first roadnetwork data;

FIG. 49C is yet another a conceptual diagram of exemplary modified firstroad network data;

FIG. 50A is a conceptual diagram of exemplary representative points ofthe first plurality of road network subzones superposed thereon;

FIG. 50B is a conceptual diagram of exemplary groups of representativepoints according to a spatial clustering algorithm;

FIG. 50C is another conceptual diagram of exemplary groups ofrepresentative points according to a spatial clustering algorithm;

FIG. 50D is yet another conceptual diagram of exemplary groups ofrepresentative points according to a spatial clustering algorithm;

FIG. 50E is yet another conceptual diagram of exemplary groups ofrepresentative points according to a spatial clustering algorithm;

FIG. 51 is a conceptual diagram of the first plurality of road networksubzones indicating the mapping of non-core subzones to intersections.

FIG. 52A is a conceptual diagram of exemplary second road network data;

FIG. 52B is another conceptual diagram of exemplary second road networkdata;

FIG. 52C is another conceptual diagram of exemplary second road networkdata;

FIG. 53A is another simplified block diagram of a traffic analyticssystem;

FIG. 53B is a conceptual diagram of third vehicle data organized bydevice ID;

FIG. 53C is a conceptual diagram of vehicle paths of vehicles;

FIG. 53D shows exemplary journey data;

FIG. 53E is a conceptual diagram of vehicle journeys;

FIG. 53F is a conceptual diagram of exemplary vehicle journeyssuperposed on a first plurality of road network subzones.

FIG. 54A is a conceptual diagram of a portion of a first plurality ofroad network subzones;

FIG. 54B is an enlarged view of a portion of a first plurality of roadnetwork subzones;

FIG. 54C is a conceptual diagram of exemplary modified journey data;

FIG. 54D is another conceptual diagram of exemplary modified journeydata;

FIG. 55A is a conceptual diagram of exemplary trip data;

FIG. 55B is conceptual diagram of a trip superposed on a portion of afirst plurality of road network subzones;

FIG. 55C is another conceptual diagram of exemplary trip data;

FIG. 55D is a conceptual diagram of exemplary trips superposed on aportion of a first plurality of road network subzones;

FIG. 55E is another conceptual diagram of exemplary trips superposed ona first plurality of road network subzones;

FIG. 56A is a conceptual diagram of exemplary trip data;

FIG. 56B is a table showing assigned cardinal direction based on thebearing;

FIG. 56C is a conceptual diagram of exemplary trip metadata;

FIG. 56D is another conceptual diagram of exemplary trip metadata;

FIG. 57A is another conceptual diagram of exemplary trip metadataassociated will all intersections or a road network;

FIG. 57B shows exemplary filter data;

FIG. 57C shows exemplary filtered trip metadata;

FIG. 57D shown exemplary intersection metrics data;

FIG. 58A is a flow diagram of a process for determining corridor metricsfor a road network corridor;

FIG. 58B is a conceptual diagram of an exemplary corridor;

FIG. 58C is a conceptual diagram of exemplary intersection metrics datafor a plurality of intersections;

FIG. 58D is a conceptual diagram of exemplary intersection metrics datafor an intersection;

FIG. 58E is a conceptual diagram of exemplary traffic metrics betweentwo intersections;

FIG. 58F is a conceptual diagram of exemplary cumulative metrics datafor a sequence of intersections;

FIG. 58G is a conceptual diagram of exemplary cumulative metrics datafor a plurality of sequences of intersections;

DESCRIPTION

Telematics is a method of monitoring a vehicle using an onboardmonitoring device for gathering and transmitting vehicle operationinformation. For instance, fleet managers employ telematics to haveremote access to real time operation information of each vehicle in afleet. A vehicle may include a car, truck, recreational vehicle, heavyequipment, tractor, snowmobile or other transportation asset. Amonitoring device may detect environmental operating conditionsassociated with a vehicle, for example, outside temperature, attachmentstatus of an attached trailer, and temperature inside an attachedrefrigeration trailer. A monitoring device may also detect operatingconditions of an associated vehicle, such as position, (e.g., geographiccoordinates), speed, and acceleration, among others.

In an exemplary telematics system, raw vehicle data, including vehicleoperation information indicative of a vehicle's operating conditions, istransmitted from an onboard monitoring device to a remote subsystem,(e.g., server). Raw vehicle data may include information indicating theidentity of the onboard monitoring device (e.g., device identifier,device ID) and/or the identity of the associated vehicle the onboardmonitoring device is aboard. Specific and non-limiting examples of rawvehicle data includes device ID data, position data, speed data,ignition state data, (e.g. indicates whether vehicle ignition is ON orOFF), and date and time data indicative of a date and time vehicleoperating conditions were logged by the monitoring device. Raw vehicledata transmitted and collected over a period of time forms historicalvehicle data which may be stored by the remote subsystem for futureanalysis of a single vehicle or fleet performance. In practise, a singlefleet may comprise many vehicles, and thus large volumes of raw vehicledata (e.g., terabytes, petabytes, exabytes . . . ) may be transmittedto, and stored by, a remote subsystem. Telematics systems are discussedin further detail below with reference to FIGS. 1A, 1B, 1C and FIG. 2.

Processing historical vehicle data corresponding to positions within aroadway section of interest may provide an alternative technique forobtaining traffic data and/or traffic metrics that avoid some of thedrawbacks of existing techniques described in the foregoing. Forexample, a method for obtaining traffic data and/or traffic metrics fromhistorical vehicle data may include obtaining a location (e.g., boundarycoordinates) of a roadway section of interest. For instance, a roadagency may store geographic data describing a roadway system comprisingthe roadway section of interest on a publicly accessible server, such asa server accessible via the Internet. The geographic data may be in theform of a geospatial file (e.g., shape file (.shp), GeoJSON (.geojson)),or other file format, from which geographical coordinates of boundariesdelineating roads forming the roadway system may be extracted. In thisexample, a geospatial file including boundary coordinates of the roadwaysection of interest is accessed, and latitude, longitude (Lat/Long)coordinates of a plurality of points defining the boundaries thereof areextracted from the geospatial file. Next, a plurality of raw vehicledata instances corresponding to a position within boundaries of theroadway section of interest are selected from the historical vehicledata and processed for providing traffic data and/or traffic metricsrelating to the roadway section of interest.

In an exemplary implementation, obtaining traffic data and/or trafficmetrics for a roadway section of interest from historical vehicle dataincludes obtaining and processing vehicle speed data for determining anaverage speed of vehicles traversing a roadway section of interest. Inthis example, the roadway section of interest is in the form of aportion of a road (i.e., a road portion.) Firstly, the location of theroad portion is determined. For instance, geographical coordinates ofboundaries of the road portion are extracted, for example, from a shapefile (.shp) or a GeoJSON file (.geojson).

As described in the foregoing, historical vehicle data comprises rawvehicle data instances corresponding to a plurality of vehicles whichmay be indicative of device ID, vehicle position, speed, and date & timethe vehicle position and speed were logged. A subset of raw vehicle datainstances corresponding to a location within the boundaries of the roadportion are selected from historical vehicle data and a cumulative speedof all vehicles that have traversed the road portion are divided by thenumber thereof to provide an average speed traffic metric. This is onlyone method of obtaining traffic data and/or traffic metrics fromhistorical vehicle data and is not intended to limit embodiments to thisexample.

In practise, locations and/or boundaries of roadway sections of interestare not readily available. For instance, some geographic informationsystems (GISs), (e.g., geographical information systems available fromESRI® of Redlands, Calif., USA), and web mapping services (e.g., GoogleMaps, developed by Google® of Mountain View, Calif., USA), among others,have compiled geospatial information describing locations, (e.g.,boundary information) of roadway systems. However, such systems andservices have invested significant resources to do so. For instance,high volumes of different data types are collected via a variety of datacollection techniques. This data is then processed to provide geospatialinformation. Some exemplary data collection techniques include aerialand satellite image capture, video recording, Light Detection andRanging (LiDAR), road surveying, and crowdsourcing.

In general, implementing similar techniques for obtaining roadwaysection locations would be time consuming, complex, computationallyintensive, and costly. Some web mapping services, (e.g., Google Maps)may provide geospatial roadway-related information, such as Lat/Longcoordinates of road boundaries, via interactive maps. However, suchfunctionality is not designed to enable easy extraction of boundaryinformation in large quantities and/or in a suitable machine-readabledata format. Alternatively, roadway boundary information may beavailable in a suitable machine-readable data format, GIS, for example,however, at a cost.

Described herein are alternative techniques for defining locations ofroadway sections that may avoid some issues of known techniquesdescribed in the foregoing. Upon definition of a location of a roadwaysection of interest, related traffic data and/or traffic metrics relatedthereto may be determined by processing raw vehicle data instancescorresponding to positions within a roadway section of interest, asdescribed above.

In general, techniques described herein may be used to determine alocation of any area frequently used by vehicles. Such areas arediscussed in further detail below in reference to FIGS. 6Ai-6Avi andFIGS. 6Bi-6Bvi.

Described herein are various embodiments of systems and methods fordefining an area frequently used by vehicles, (i.e., an area on theEarth's surface repeatedly employed by vehicles), hereinafter referredto as a ‘vehicle way’. A vehicle way may include an area used byvehicles for movement and/or parking. Specific and non-limiting examplesof vehicle ways include traffic-designated areas, such as by roadagencies, for channeling traffic flow (e.g., roads, traffic junctions),and for parking (e.g., street parking spaces, commercial parking lots).Vehicle ways may also include areas that are not traffic-designatedareas. For instance, areas that have not been created and/or maintainedby a road agency or commercial entity for vehicle use, nonetheless arerepeatedly used thereby. For example, a vehicle way includes an ad hocvehicle way. An example of an ad hoc vehicle way includes a beaten pathcreated by frequent flow of vehicles for accessing a natural attraction,such as a lake, river, or forested area, for which no access road wasavailable. Another example of an ad hoc vehicle way includes a portionof a field frequently used to accommodate overflow vehicles of a nearbyparking lot.

Illustrated in FIGS. 6Ai-6Avi are simplified diagrams of variousexemplary types of vehicle ways, including: circular traffic junction602, (i.e., roundabout) having circular road segment 604 and roadsegments 606, 607, 608 and 609 for channeling vehicles therethrough;three way traffic junction 612 (i.e., intersection) having road segments614, 616, and 617 for channeling vehicles therethrough; traffic junction618 (i.e., highway off-ramp) having road segment 619, main road portion620, and 622 for channeling vehicles therethrough; parking lot 624,having parking area 626 and entry/exit 629 for channelling vehicles inand out thereof; road portion 650 having opposing lanes 643 a and 643 b;and on-street parking space 644. The exemplary vehicle ways of FIGS.6Ai-6Avi are provided for example purposes only and embodiments are notintended to be limited to the examples described herein.

A defined vehicle way may be described by any data format provided thedata indicates the location (e.g., the unique location of the vehicleway on the Earth's surface) occupied thereby. For example, a vehicle waymay be defined by a plurality of points defining the boundaries of thevehicle way. The geographic coordinates of the plurality of points maybe, for example, stored in a text file, such as a comma-separated values(.csv) file. In another example, boundaries may be described in ageospatial file, for instance a shape file (.shp) or a GeoJSON file(.geojson), from which geographic coordinates of vehicle way boundariesmay be obtained. In yet another example, the location occupied by avehicle way may be described in accordance with a geospatial indexingsystem, such as Geohash. Geohash is a known public domain hierarchicalgeospatial indexing system which uses a Z-order curve to hierarchicallysubdivide the latitude/longitude grid into progressively smaller cellsof grid shape. For instance, a Geohash string indicates a uniquegeographical area (e.g., cell). A vehicle way may be described by dataindicative of a plurality of Geohash strings indicating the Geohashcells occupied by the vehicle way. A vehicle way may be described innumerous data formats and embodiments are not intended to be limited toexamples described herein.

Some embodiments described herein relate to techniques for defining avehicle way comprising processing data indicative of vehicle operationconditions of a plurality of vehicles that have travelled within a knownarea, that is, an area of which the location thereof is defined.Processing such data may provide an indication as to whether the knownarea is a portion of the vehicle way. In other words, processing suchdata may provide an indication as to whether the vehicle way occupiesthe known area.

Processing data may also include processing other data indicative ofvehicle operation conditions of another plurality of vehicles that havetravelled within other known areas proximate the known area.Furthermore, processing data may also include processing spatialrelationship data of the known area to other known areas proximatethereto.

Some embodiments described herein relate to defining a vehicle way bydefining a relationship between vehicle operating conditions of vehiclesthat have operated proximate known areas and the likelihood the knownareas are portions of a vehicle way.

Some embodiments described herein relate to techniques for defining avehicle way using machine learning techniques using historical vehicledata, (e.g., raw vehicle data), and/or data derived therefrom to definethe location of the vehicle way.

In example implementations, a traffic analytics system may be configuredto access historical vehicle data associated with known areas and defineone or more classification models that are related to operatingconditions of corresponding vehicles, and then operate in accordancewith the one or more models. In general, each such classification modelmay receive as input, data (i.e., features), derived from historicalvehicle data related to vehicles that have operated within a known area,within a plurality of known areas, and a spatial relationship of theknown area to the other known areas, and output an indication the knownarea is a portion of a vehicle way.

As described in the foregoing, a vehicle way includes areas frequentlyused and/or employed by vehicles. The phrases ‘frequently used’ and‘repeatedly employed’ are relative to the time period of logging of thehistorical vehicle data. For example, data (i.e., features), derivedfrom historical vehicle data related to vehicles that have travelledwithin a known area are input to a classification model for use thereof.However, if there is little raw vehicle data corresponding to a vehicleway of interest within the historical vehicle data, the output of theclassifier may not provide meaningful data when applied for defining thevehicle way of interest.

Illustrated in FIG. 1A is a simplified diagram of an exemplary networkconfiguration 100 with which some embodiments may operate. Networkconfiguration 100 includes telematics system 102, traffic analyticssystem 104, remote system 106, and communication network 110.Communication network 110 may be communicatively coupled to telematicssystem 102, traffic analytics system 104, and remote system 106,enabling communication therebetween.

For example, traffic analytics system 104 may communicate withtelematics system 102, and remote system 106 for receiving historicalvehicle data or a portion thereof via communication network 110. FIG. 1Bis a simplified diagram of network configuration 100 illustratingcommunication path 112 between traffic analytics system 104 andtelematics system 102 and communication path 113 between trafficanalytics system 104 and remote system 106.

FIG. 1C is a simplified diagram of another exemplary networkconfiguration 101 with which some embodiments may operate. Networkconfiguration 101 includes telematics system 102, traffic analyticssystem 104, remote system 106, data management system 108 andcommunication network 110. Communication network 110 may becommunicatively coupled to telematics system 102, traffic analyticssystem 104, remote system 106, and data management system 108, enablingcommunication therebetween.

For example, telematics system 102 may transmit raw vehicle data and/orhistorical vehicle data to data management system 108 for the storagethereof, as illustrated by communication path 114. Traffic analyticssystem 104 may be configured for communicating with data managementsystem 108, for receiving historical vehicle data or a portion thereofvia communication network 110, as illustrated by communication path 116.Traffic analytics system 104 may also be configured for communicatingwith remote system 106.

Remote system 106 may be another telematics system from which trafficanalytics system 104 receives historical vehicle data. Alternatively,remote system 106 may store historical vehicle data collected by one ormore telematics systems and/or similar vehicle monitoring systems.

Alternatively, remote system 106 may provide external data to trafficanalytics system 104. For example, remote system 106 is a map serviceprovider that provides geospatial information regarding roadway systems,traffic control equipment, and/or jurisdictional boundary information,among other geospatial information to traffic analytics system 104.

In yet another example, remote system 106 may be a customer system towhich traffic analytics system 104 transmits output data in the form ofraw data, a web page, or in another data format.

Communication network 110 may include one or more computing systems andmay be any suitable combination of networks or portions thereof tofacilitate communication between network components. Some examples ofnetworks include, Wide Area Networks (WANs), Local Area Networks (LANs),Wireless Wide Area Networks (WWANs), data networks, cellular networks,voice networks, among other networks, which may be wired and/orwireless. Communication network 110 may operate according to one or morecommunication protocols, such as, General Packet Radio Service (GPRS),Universal Mobile Telecommunications Service (UMTS), Global System forMobile (GSM), Enhanced Data Rates for GSM Evolution (EDGE), Long TermEvolution (LTE), CDMA (Code-division Multiple Access) (CDMA), WCDMA(Wide Code-division Multiple Access), (High Speed Packet Access (HSPA),Evolved HSPA (HSPA+), Low-power WAN (LPWAN), Wi-Fi, Bluetooth, Ethernet,Hypertext Transfer Protocol Secure (HTTP/S), Transmission ControlProtocol/Internet Protocol (TCP/IP), and Constrained ApplicationProtocol/Datagram Transport Layer Security (CoAP/DTLS), or othersuitable protocol. Communication network 110 may take other forms aswell.

Illustrated in FIG. 2 is a simplified block diagram of an exemplarytelematics system for gathering and storing vehicle operationinformation. Telematics system 102 comprises telematics subsystem 210(e.g., server) having a first network interface 206 and onboardmonitoring devices 202, 203, and 204 communicatively coupled therewithvia communication network 207.

Communication network 207 may include one or more computing systems andmay be any suitable combination of networks or portions thereof tofacilitate communication between network components. Some examples ofnetworks include, Wide Area Networks (WANs), Local Area Networks (LANs),Wireless Wide Area Networks (WWANs), data networks, cellular networks,voice networks, among other networks, which may be wired and/orwireless. Communication network 207 may operate according to one or morecommunication protocols, such as, General Packet Radio Service (GPRS),Universal Mobile Telecommunications Service (UMTS), Global System forMobile (GSM), Enhanced Data Rates for GSM Evolution (EDGE), Long TermEvolution (LTE), CDMA (Code-division Multiple Access) (CDMA), WCDMA(Wide Code-division Multiple Access), (High Speed Packet Access (HSPA),Evolved HSPA (HSPA+), Low-power WAN (LPWAN), Wi-Fi, Bluetooth, Ethernet,Hypertext Transfer Protocol Secure (HTTP/S), Transmission ControlProtocol/Internet Protocol (TCP/IP), and Constrained ApplicationProtocol/Datagram Transport Layer Security (CoAP/DTLS), or othersuitable protocol. Communication network 110 may take other forms aswell.

Telematics system 102 may comprise another network interface 208 forcommunicatively coupling to another communication network, such ascommunication network 110. Telematics subsystem 210 may comprise aplurality of servers, datastores, and other devices, configured in acentralized, distributed or other arrangement.

Also shown in FIG. 2 are vehicles 212, 213 and 214, each thereof havingaboard the monitoring devices 202, 203, and 204, respectively. A vehiclemay include a car, truck, recreational vehicle, heavy equipment,tractor, snowmobile, or other transportation asset. Onboard monitoringdevices 202-204 may transmit raw vehicle data associated with vehicles212-214. Raw vehicle data transmitted and collected over a period oftime forms historical vehicle data which may be stored by remotetelematics subsystem 210.

In practise, a monitoring device is associated with a particularvehicle. For example, during configuration of monitoring devices202-204, each thereof may be assigned a unique device ID that isuniquely associated with a vehicle information number (VIN) of vehicles212-214, respectively. This enables an instance of received raw vehicledata to be associated with a particular vehicle. As such,vehicle-specific raw vehicle data may be discernable among other rawvehicle data in the historical vehicle data.

Three monitoring devices are described in this example for explanationpurposes only and embodiments are not intended to be limited to theexamples described herein. In practise, a telematics system may comprisemany vehicles, such as hundreds, thousands and tens of thousands ormore. Thus, huge volumes of raw vehicle data may be received and storedby telematics subsystem 210.

In general, monitoring devices comprise sensing modules configured forsensing and/or measuring a physical property that may indicate anoperating condition of a vehicle. For example, sensing modules may senseand/or measure a vehicle's position, (e.g., GPS coordinates), speed,direction, rates of acceleration or deceleration, for instance, alongthe x-axis, y-axis, and/or z-axis, altitude, orientation, movement inthe x, y, and/or z direction, ignition state, transmission and engineperformance, among others. One of ordinary skill in the art willappreciate that these are but a few types of vehicle operatingconditions that may be detected.

Monitoring device 202 may comprise a sensing module for determiningvehicle position. For instance, the sensing module may utilize GlobalPositioning System (GPS) technology (e.g., GPS receiver) for determiningthe geographic position (Lat/Long coordinates) of vehicle 212.Alternatively, the sensing module utilizes another global navigationsatellite system (GNSS) technology, such as, GLONASS or BeiDou.Alternatively, the sensing module may further utilize another kind oftechnology for determining geographic position. In addition, the sensingmodule may provide other vehicle operating information, such as speed.

Alternatively, vehicle position information may be provided according toanother geographic coordinate system, such as, Universal TransverseMercator, Military Grid Reference System, or United States NationalGrid.

In general, a vehicle may include various control, monitoring and/orsensor modules for detecting vehicle operating conditions. Some specificand non-limiting examples include, an engine control unit (ECU), asuspension and stability control module, a headlamp control module, awindscreen wiper control module, an anti-lock braking system module, atransmission control module, and a braking module. A vehicle may haveany combination of control, monitoring and/or sensor modules. A vehiclemay include a data/communication bus accessible for monitoring vehicleoperating information, provided by one or more vehicle control,monitoring and/or sensor modules. A vehicle data/communication bus mayoperate according to an established data bus protocol, such as theController Area Network bus (CAN-bus) protocol that is widely used inthe automotive industry for implementing a distributed communicationsnetwork. Specific and non-limiting examples of vehicle operationinformation provided by vehicle monitoring and/or sensor modulesinclude, ignition state, fuel tank level, intake air temp, and engineRPM among others.

Monitoring device 202 may comprise a monitoring module operable tocommunicate with a data/communication bus of vehicle 212. A monitoringmodule may communicate via a direct connection, such as, electricallycoupling, with a data/communication bus of vehicle 212 via a vehiclecommunication port, (e.g., diagnostic port/communication bus, OBDIIport). Alternatively, a monitoring module may comprise a wirelesscommunication interface for communicating with a wireless interface ofthe data/communication bus of vehicle 212. Optionally, a monitoringmodule may communicate with other external devices/systems that detectoperating conditions of the vehicle.

Monitoring device 202 may be configured to wirelessly communicate withtelematics subsystem 210 via a wireless communication module. In someembodiments, monitoring device 202 may directly communicate with one ormore networks outside vehicle 212 to transmit data to telematicssubsystem 210. A person of ordinary skill will recognize thatfunctionality of some modules may be implemented in one or more devicesand/or that functionality of some modules may be integrated into thesame device.

Monitoring devices 202-204 may transmit raw vehicle data, indicative ofvehicle operation information collected thereby, to telematics subsystem210. The raw vehicle data may be transmitted at predetermined timeintervals, (e.g. heartbeat), intermittently, and/or according to otherpredefined conditions. Raw vehicle data transmitted from monitoringdevices 202-204 may include information indicative of device ID,position, speed, ignition state, and date and time operating conditionsare logged, for instance, in an onboard datastore. One of ordinary skillin the art will appreciate that raw vehicle data may comprise dataindicative of numerous other vehicle operating conditions. Raw vehicledata may be transmitted from a monitoring device when a vehicle ismoving, stationary, and during both ON and OFF ignition states.

In an exemplary implementation, raw vehicle data received and stored bya subsystem over a period of time forms historical vehicle data. In anexemplary implementation, historical vehicle data may be stored bytelematics subsystem 210 in a database, such as database 209, as shown.A period of time may include, for example, 3 months, 6 months, 12months, or another duration of time.

Traffic Analytics System

Illustrated in FIG. 3A and FIG. 3B there are two exemplary trafficanalytics systems 104 including traffic analytics system 104 a andtraffic analytics system 104 b, as shown respectively.

FIG. 3A is a simplified block diagram of exemplary traffic analyticssystem 104 a comprising a processing resource 302, datastore 304, andnetwork interface 306. For example, processing resource 302 anddatastore 304 may be communicatively coupled by a system communicationbus, a wired network, a wireless network, or other connection mechanismand arranged to carry out various operations described herein.Optionally, two or more of these components may be integrated togetherin whole or in part.

Network interface 306 may be interoperable with communication network110 and may be configured to receive data from various networkcomponents of the network configuration 100, 101 such as telematicssystem 102, remote system 106, data management system 108, and possiblyother network components. Traffic analytics system 104 a, 104 b maycommunicate with one or more of these network components for obtaininghistorical vehicle data, or portions thereof. For instance, oncereceived, datastore 304 may store subsets of raw vehicle data in adatabase, such as database 309.

In an exemplary implementation, traffic analytics system 104 a isconfigured to interoperate with data management system 108 for obtaininghistorical vehicle data and/or a portion thereof. For example, datamanagement system 108 may manage and store large volumes (e.g., bigdata) and multiple types of data. Data management system 108 maycomprise a relational database, for storing historical vehicle data, ora portion thereof, collected by one or more telematics or vehiclemonitoring systems. Data management system 108 may include a web servicethat enables interactive analysis of large datasets stored in a remotedatastore. Traffic analytics system 104 a may be configured tointeroperate with such a data management system for obtaining rawvehicle data from historical vehicle data stored therein and managedthereby. An example of such a data management system is a managed clouddata warehouse for performing analytics on data stored therein, such asBigQuery™, available from Google® of Mountain View, Calif., USA.

FIG. 3B is a simplified block diagram of second exemplary trafficanalytics system 104 b comprising processing resource 302, datastore304, data management system 305 and network interface 306. For example,processing resource 302, datastore 304, and data management system 305may be communicatively coupled by a system communication bus, a wirednetwork, a wireless network, or other connection mechanism and arrangedto carry out various operations described herein. Optionally, two ormore of these components may be integrated together in whole or in part.Data management system 305 may comprise a datastore including databasefor storing historical vehicle data or a portion thereof. Optionallydata management system 305 stores and manages large volumes (e.g., bigdata) and multiple types of data. For example, data management system305 may comprise a relational database, for storing historical vehicledata collected by one or more telematics or vehicle monitoring systems,or a portion thereof. In another example, database 309 of datamanagement system 305 stores subsets of raw vehicle data from historicalvehicle data for processing by traffic analytics system 104 b.Alternatively, data management system 305 may include and/or access aweb service that enables interactive analysis of large datasets storedin a remote datastore. An example of such a data management system is amanaged cloud data warehouse for performing analytics on data storedtherein, such as BigQuery™.

According to an embodiment, exemplary traffic analytics system 104 breceives and stores historical vehicle data in data management system305 and operates on subsets of historical vehicle data in accordancewith operations described herein.

Processing resource 302 may include one or more processors and/orcontrollers, which may take the form of a general or a special purposeprocessor or controller. In exemplary implementations, processingresource 302 may be, or include, microprocessors, microcontrollers,application specific integrated circuits, digital signal processors,and/or other data processing devices. Processing resource 302 may be asingle device or distributed over a network.

Datastore 304 may be or include one or more non-transitorycomputer-readable storage media, such as optical, magnetic, organic, orflash memory, among other data storage devices and may take any form ofcomputer readable storage media. Datastore 304 may be a single device ormay be distributed over a network.

Processing resource 302 may be configured to store, access, and executecomputer-readable program instructions stored in datastore 304, toperform the operations of traffic analytics system 104 a, 104 bdescribed herein. For instance, processing resource 302 may beconfigured to receive historical vehicle data and may execute aclassification model for defining a vehicle way. Other functions aredescribed below.

Traffic analytics system 104 a, 104 b may be configured to access,receive, store, analyze and process raw vehicle data for defining aclassification model and/or executing a classification model. Forexample, traffic analytics system 104 a, 104 b may select and processraw vehicle data of a plurality of vehicles corresponding to a knownarea, for determining whether the known area is likely to be a portionof a vehicle way. Other examples and corresponding operations are alsopossible.

In some example implementations, traffic analytics system 104 a, 104 bmay include and/or communicate with a user interface. The user interfacemay be located remote from traffic analytics system 104 a, 104 b. Forinstance, traffic analytics system 104 a, 104 b may communicate with auser interface via network interface 306. Other examples are alsopossible.

For the ease of description, traffic analytics system 104 a, 104 b isshown as a single system, however, it may include multiple computingsystems, such as servers, storage devices, and other distributedresources, configured to perform operations/processes described herein.Operations and processes performed by traffic analytics system 104 a,104 b described herein may be performed by another similarly configuredand arranged system.

In an exemplary implementation, traffic analytics system 104 a, 104 b isconfigured to obtain, store and process historical vehicle data. Forexample, traffic analytics system 104 a, 104 b obtains historicalvehicle data 404 from telematics system 102 and stores it in database309. FIG. 4A is a conceptual diagram of database 309. In this example,traffic analytics system 104 a, 104 b organizes historical vehicle data404 by vehicle, via associated device ID. For instance, vehicle-specificdatasets 412-414 of database 309 comprise raw vehicle data indicative ofvehicle operation information of vehicles 212-214, respectively.

Shown in FIG. 4B is a conceptual diagram of dataset 412. In thisexample, each row thereof represents a raw vehicle data instance 406indicative of vehicle operation information collected by monitoringdevice 202 at different points in time. Raw vehicle data instances 406of dataset 412 are organized sequentially in time, from DT0 to DT5. Inthis example, a raw vehicle data instance 406 includes device ID data,speed data, position data, (e.g., LAT/LONG), ignition state data, anddate and time data, (e.g., timestamp), as shown.

Now referring to FIG. 4C, shown is a conceptual diagram of vehicle 212'spath 416 within geographic area 415 corresponding to vehicle positiondata 418 of dataset 412. Vehicle-position data points 420 represents aposition of vehicle 212 at different points in time, DT0-DT5. As shown,the position of vehicle 212 changes position at each temporallysubsequent point in time.

For ease of description, database 309 comprising historical vehicle data404 is described as organized into vehicle-specific datasets 412-414.One of ordinary skill appreciates that historical vehicle data may beorganized in numerous manners.

Intelligent Telematics System

An intelligent telematics system includes aspects of a telematics systemand a traffic analytics system, such as, telematics system 102 andtraffic analytics system 104 a, 104 b.

FIG. 5A is a simplified diagram of an alternative embodiment with whichsome embodiments may operate. Shown in FIG. 5A is intelligent telematicssystem 500 a comprising monitoring devices 202, 203, and 204, telematicssubsystem 210 (e.g., server), traffic analytics system 104 a,communicatively coupled via communication network 207. Intelligenttelematics system 500 a may also include network interface 506compatible for interfacing with a communication network forcommunicating with other network components. For example, networkinterface 506 may be interoperable with communication network 110 andmay be configured to receive data from various network components of thenetwork configuration 100, 101 such as remote system 106.

In this example monitoring devices 202-204 may be configured towirelessly communicate with telematics subsystem 210 via a wirelesscommunication module. In some embodiments, monitoring devices 202-204may directly communicate with one or more networks outside respectivevehicles to transmit data to telematics subsystem 210. A person ofordinary skill will recognize that functionality of some modules may beimplemented in one or more devices and/or that functionality of somemodules may be integrated into the same device.

Monitoring devices 202-204 may transmit raw vehicle data, indicative ofvehicle operation information collected thereby, to telematics subsystem210, as represented by communication path 510. In an exemplaryimplementation, raw vehicle data received and stored by telematicssubsystem 210 over a period of time forms historical vehicle data. Forinstance, historical vehicle data may be stored by telematics subsystem210 in database 209. A period of time may include, for example, 3months, 6 months, 12 months, or another duration of time. In anexemplary embodiment, subsets of raw vehicle data selected fromhistorical vehicle data stored in database 209 may be stored in anotherdatabase, for instance, database 309 for processing by traffic analyticssystem 104 a. In this example raw vehicle data is transmitted bytelematics subsystem 210 and received by traffic analytics system 104 avia communication path 512, as shown.

FIG. 5B is a simplified diagram of another alternative embodiment withwhich some embodiments may operate. Shown in FIG. 5B is intelligenttelematics system 500 b comprising monitoring devices 202, 203, and 204and traffic analytics system 104 b, communicatively coupled therewithvia communication network 207.

Intelligent telematics system 500 b may also include network interface506 compatible for interfacing with a communication network forcommunicating with other network components. For example, networkinterface 506 may be interoperable with communication network 110 andmay be configured to receive data from various network components of thenetwork configuration 100, 101, such as remote system 106.

In this example monitoring devices 202-204 may be configured towirelessly communicate with traffic analytics system 104 b via awireless communication module. In some embodiments, monitoring devices202-204 may directly communicate with one or more networks outsiderespective vehicles to transmit data to traffic analytics system 104 b.A person of ordinary skill will recognize that functionality of somemodules may be implemented in one or more devices and/or thatfunctionality of some modules may be integrated into the same device.

Monitoring devices 202-204 may transmit raw vehicle data, indicative ofvehicle operation information collected thereby, to traffic analyticssystem 104 b via communication path 514, as shown. In an exemplaryimplementation, raw vehicle data received and stored by trafficanalytics system 104 b over a period of time forms historical vehicledata. For instance, historical vehicle data may be stored by trafficanalytics system 104 b in database 209 in data management system 305. Aperiod of time may include, for example, 3 months, 6 months, 12 months,or another duration of time. In an exemplary embodiment, subsets of rawvehicle data selected from historical vehicle data stored in database209 may be stored in another database, for instance, database 309 forprocessing by traffic analytics system 104 b. In this example rawvehicle data is transmitted by telematics subsystem 210 and received bytraffic analytics system 104 a. Traffic analytics system 104 b may beconfigured to perform operations of telematics system 201 as describedherein.

In some example implementations, intelligent telematics system 500 a,500 b may be configured to include and/or communicate with a userinterface. The user interface may be located remote therefrom. Forinstance, intelligent telematics system 500 a, 500 b may communicatewith a user interface via network interface 506. Other examples are alsopossible.

Classification Model

According to an embodiment, a classifier defining a relationship betweenoperation of a plurality of vehicles having operated in a known area anda probability the known area is a portion of a vehicle way may bedefined by processing corresponding historical vehicle data. Forexample, such processing may provide features (e.g., data indicative ofvariables/attributes, or measurements of properties) of the known area.A machine learning algorithm may be trained with training datacomprising features to recognize patterns therein and generalize arelationship between the features and an outcome that the known area isoccupied by the vehicle way.

Shown in FIG. 7 is a flow diagram of a process 700 for defining aclassifier for use in defining a vehicle way according to someembodiments. In particular, a classifier is for providing an indicationthat a known area is, or is not, a portion of a vehicle way. Aclassifier may provide as output a likelihood (e.g., probability) aknown area is a portion of a vehicle way. Alternatively, a classifiermay output an indication (e.g., binary value) that a known area is, oris not, a portion of a vehicle way.

Process 700 is described below as being carried out by traffic analyticssystem 104 a. Alternatively, process 700 may be carried out by trafficanalytics system 104 b, intelligent telematics system 500 a, 500 b,another system, a combination of other systems, subsystems, devices orother suitable means provided the operations described herein areperformed. Process 700 may be automated, semi-automated and some blocksthereof may be manually performed.

Block 701

Process 700 begins at block 701, wherein a plurality of sample vehicleways is identified. According to an embodiment, a classifier may bedefined according to a type and/or subtype of vehicle way that is to bedefined using the classifier. Specific and non-limiting examples oftypes of vehicle ways include, traffic junctions, road segments, parkinglots, and ad hoc vehicle ways. Specific and non-limiting examples ofsubtypes of vehicle ways include: subtypes of traffic junctions,including roundabouts, intersections, on-ramps, and off-ramps; subtypesof parking lots including single entry and single exit parking lots,single entry and multiple exit parking lots, and multiple entry andsingle exit parking lots; and subtypes of road segments including oneway, two way, multi-lane, and divided highways. A subtype may beconsidered another type of vehicle way. A subtype may be consideredanother type of vehicle way. Block 701 will be further described belowin reference to FIG. 8.

In an exemplary implementation, a plurality of sample vehicle ways ofonly parking lot type is identified for defining a classifier for use indefining parking lots. In another exemplary implementation, a pluralityof sample vehicle ways for defining a classifier for use in defining avehicle way in the form of a traffic junction comprises trafficjunctions only. For example, the plurality of sample vehicle ways mayinclude one or more of the following traffic junctions, 3-wayintersections, 4-way intersections, n-way intersections, roundabouts,and any other portion of a road system where multiple roads intersectallowing vehicular traffic to change from one road to another.

Alternatively, a plurality of sample vehicle ways for defining aclassifier for use in defining a subtype of a traffic junction in theform of an intersection only comprises intersections (e.g., 3-wayintersections, 4-way intersections). Defining a classifier for use fordefining a particular type or subtype of vehicle way may provide a moreaccurate classifier.

Furthermore, a classifier defined with sample vehicle ways of only onetype and/or one subtype of vehicle way may be suitable for use indefining vehicle ways of a different type and/or subtype of vehicle way.

Alternatively, a classifier may be defined for defining all types ofvehicle ways.

In an exemplary implementation, a classifier for use in defining avehicle way in the form of an intersection is defined according toprocess 700. In FIG. 8, shown is a simplified diagram of area 800comprising a roadway system including a plurality of sample vehicle waysin the form of sample intersections 802 a-802 f. Area 800 also comprisesparking lots 805, roadway sections 806, and non-traffic designated areas807 (e.g., greenspace, sidewalks). For ease of description, only sixsample intersections are described in this example. In practise,however, the number of sample vehicle ways may include other than sixsample vehicle ways.

In this example, sample intersections 802 a-802 f are also shown to bepart of the same roadway network. However, sample vehicle ways may beselected from different roadway systems located in different cities,countries and/or continents. One of ordinary skill in the artappreciates that selection of an appropriate number and type(s) ofsample vehicle ways will become apparent during definition (e.g.,training and verification) of the classifier.

Block 702

Once a plurality of sample vehicle ways has been identified, a pluralityof associated zones for each of the plurality of sample vehicle ways, isdefined in block 702. Block 702 will be further described below inreference to FIGS. 6Ai-6Avi, 6Bi-6Bvi, 6C, FIG. 8, FIG. 9, FIGS.10A-10B, FIGS. 11A-11F, FIGS. 12A-12D, and FIGS. 13A-13B.

A zone includes an area encompassing an associated vehicle way. Forexample, shown in FIG. 6C is a conceptual diagram of elements of avehicle way, including a bridge element 670 coupled to an employedelement 671. For instance, bridge element 670 comprises an area fortraversal of a vehicle for transitioning therefrom to employed element671. Employed element 671 comprises an area in which the vehiclemanoeuvres, such as for moving and/or parking. A vehicle way may haveone or more of each of bridge elements and employed elements.

Shown in FIG. 6Bi-6Bvi are conceptual diagrams of some specific andnon-limiting examples of zones encompassing the vehicle ways of FIG.6Ai-6Avi.

For example, zone 611 encompasses circular traffic junction 602.Elements of circular traffic junction 602 include bridge elements 628 ofroad segments 606-609 and employed element 630 of circular road segment604. A vehicle traverses one of bridge elements 628 to enter (i.e.,transition into) employed element 630. Once inside employed element 630,the vehicle moves therethrough (i.e., maneuvers) and exits (i.e.,transitions through) employed element 630 via a bridge element 628. Inthis example, circular traffic junction 602 comprises four bridgeelements 628 and one employed element 630.

Zone 613 encompasses intersection 612. Elements of intersection 612includes three bridge elements 632 for instance, road segments 614, 616,and 617 and one employed element 634 where road segments 614, 616 and617 intersect.

Zone 615 encompasses traffic junction 618. Elements of traffic junction618 includes two bridge elements 638 including off-ramp portion 622 andmain road portion 620 and one employed element such as off-ramp portion648.

Zone 640 encompasses parking lot 624. Elements of parking lot 624includes a bridge element 642 of entry/exit 629 and an employed element668 including parking area 626.

Zone 652 encompasses road portion 650. Elements of road portion 650includes two bridge elements 654 of road portions 650 and an employedelement 656 of road portion 650.

Zone 660 encompasses a bridge element 662 of on-street parking space 644and an employed element 664 of on-street parking space 644.

In some instances, elements may include physical boundaries of a roadsurface such as a sidewalk, road shoulder, and lane divider, amongothers. In other instances, a vehicle way may not have any physicalboundaries, such as a beaten path created by frequent flow of vehiclesfor accessing a natural attraction as described above.

One of ordinary skill in the art appreciates that the dimensions of azone is selected to encompass and include elements of a vehicle way. Adimension that is too small and does not include the elements, orincludes partial elements, of a vehicle way should be avoided. Forexample, referring to FIG. 6Bii, shown is zone 666 encompassing partialelements of intersection 612—only portions of bridge elements 632 andemployed element 634. In this instance, as zone 666 encompasses aninsufficient portion of intersection 612, only a portion of historicalvehicle data associated therewith will be processed for defining aclassifier. As such, the performance of the defined classifier may bepoor.

Alternatively, a dimension that is too large should also be avoided. Forinstance, a zone should be large enough to encompass a vehicle way,however, not too large such that it includes extraneous areas. Forexample, a zone that is too large may result in unnecessary processingof extraneous historical vehicle data. Furthermore, dimensions of a zonemay affect computing resources and processing time for defining and/orusing a classification model. One of ordinary skill will appreciate thatoptimal zone dimensions will become apparent during definition of theclassifier.

Continuing at block 702, each zone encompassing a vehicle way comprisesa plurality of contiguous known areas, also referred to herein assubzones. Each subzone may have boundaries defined in accordance with ageographic coordinate system representing a unique two-dimensional spaceon the Earth's surface. For example, a zone may be partitioned bysubdividing the zone into a grid of contiguous subzones bound by pairsof latitude and longitude lines. As such, the unique location of eachsubzone is known. Each subzone in the plurality of contiguous subzoneswithin a zone shares a boundary with at least one other subzone. Theplurality of contiguous subzones serves to facilitate organization ofall points therein as each subzone comprises an aggregate of a portionof points within a zone. A point located within boundaries of a subzonemay be uniquely associated therewith.

Optionally, a subzone may include a portion of a vehicle way overlappinganother portion of a vehicle way. For instance, the subzone mayrepresent a portion of an overpass that overlaps a portion of a roadwaythereunder. Optionally, a subzone may include a portion of a vehicle wayoverlapped by another portion of a vehicle way. For instance, thesubzone may represent a portion of a roadway that is overlapped by aportion of an overpass.

In some exemplary implementations, zone dimensions may be determinedaccording to the shape and size of an associated vehicle way. FIG. 9 isa simplified diagram of a plurality of exemplary zones 908 a-908 fdefined for sample intersections 802 a-802 f, respectively. In thisexample, the dimensions of each zone are such that the elements of eachassociated vehicle way are encompassed thereby. As sample vehicle ways,e.g., sample intersections 802 a-802 f vary relatively in shape andsize, so may respective zones 908 a-908 f.

Each of the plurality of zones 908 a-908 f is partitioned into aplurality of contiguous subzones 910 a-910 f. For instance, each ofzones 908 a-908 f may be subdivided into a grid of contiguous subzonesbound by pairs of latitude and longitude lines. As each of zones 908a-908 f may be different in dimension, each thereof may comprise adifferent number of subzones 910 a-910 f, as shown. For example, zone908 b is smaller than zone 908 f and accordingly, has fewer subzones 910b than the number of subzones 910 f in zone 908 f.

In some embodiments geographic coordinate data of a location (e.g.,LAT/LONG) of a reference point proximate each of the sample vehicle waysis obtained by traffic analytics system 104 a. A reference pointindicates a general area in which a vehicle way may be located.

In an exemplary implementation, a user may view a georeferenced map ofarea 800 on a display and manually identify a reference point proximatesample intersection 802 a. For instance, the georeferenced map may beaccessed via a web page of an online map service, such as Google Maps.The user may choose reference point 803 a on or near sample intersection802 a, as shown in FIG. 8. The location of reference point 803 a may beobtained, by the user selecting reference point 803 a on thegeoreferenced map with a pointer, causing text indicating geographiccoordinates (e.g., LAT/LONG) thereof to appear on the display.Alternatively, a location of a reference point proximate a vehicle waymay be obtained through use of a GPS enabled device or anothergeographic coordinate sensing device. One of ordinary skill in the artappreciates that there are various ways to obtain a location of a point.Point data indicative of the location of reference point 803 a may beprovided to traffic analytics system 104 a, for example, via a userinterface or data file accessed by traffic analytics system 104 a. Thereference point may be at or near the centre point of a zone.

In an exemplary implementation, a zone may be defined by subdividing areference area into a grid of contiguous subzones according to ahierarchical geospatial indexing system, such as Geohash. Geohash is aknown public domain hierarchical geospatial indexing system which uses aZ-order curve to hierarchically subdivide the latitude/longitude gridinto progressively smaller cells of grid shape. Each cell is rectangularand represents an area bound by a unique pair of latitude and longitudelines corresponding to an alphanumeric string, known as a Geohash stringand/or Geohash code.

In a Geohash system the size of a cell depends on a user defined lengthof a string. The hierarchical structure of Geohash grids progressivelyrefines spatial granularity as the length of string increases. Forexample, shown in FIG. 11A is table 1102 defining the relationshipbetween string length and approximate cell dimensions. As string lengthincreases, cell dimensions decrease, as shown. Cell size is alsoinfluenced by a cell's longitudinal location. Cell width reduces movingaway from the equator (to 0 at the poles) due to the nature oflongitudinal lines converging as they extend away therefrom. Table 1102provides an approximation of Geohash cell dimensions located along theequator.

Some exemplary Geohash system functions will now be described below inreference to FIGS. 11B-11G. FIG. 11F is a simplified conceptual diagramof cell 1132, defined by latitude and longitude lines 1134 and 1136,respectively. Shown in FIG. 11B is a simplified functional block diagramof an exemplary Geohash encode function 1108, for mapping a point to acell. For example, LAT/LONG coordinates, ‘42.620578, −5.620343,’ ofpoint 1130 of FIG. 11F and a user defined length=5 are input to encodefunction 1108 which maps point 1130 to cell 1132. Encode function 1108outputs string ‘ers42’ corresponding to cell 1132 having dimensions 4.89km×4.89 km. One of ordinary skill appreciates that encode function 1108will map any point within cell 1132 to the same string, ‘ers42.’

FIG. 11C is a simplified functional block diagram of an exemplaryGeohash decode function 1110 for resolving a string to the centre pointof the corresponding cell. For example, string ‘ers42’ is input todecode function 1110 and decoded to cell 1132 centre point 1135 atLAT/LONG coordinates ‘42.60498047, −5.60302734.’ In contrast to encodefunction 1108, decode function 1110 resolves an input string to LAT/LONGcoordinates of one point only, specifically, the centre point of thecorresponding cell.

FIG. 11D is a simplified functional block diagram of an exemplaryGeohash bounds function 1112 for resolving a string into °N/°S, °W/°Eline pairs that bound the corresponding cell. For example, string‘ers42’ is input to bounds function 1112 which outputs (42.626953125° N,42.5830078125° N), (−5.5810546875° E, −5.625° E) line pairs boundingcell 1132, as shown in FIG. 11F.

FIG. 11E is a simplified functional block diagram of an exemplaryGeohash neighbours function 1114 for determining the closest 8neighbouring (e.g., adjacent) cells to a given cell. For example, string‘ers42’ is input into neighbours function 1114 which outputs strings ofthe closest 8 neighbouring cells at relative positions NW, W, NE, W, E,SW, S, and SE to cell 1132. FIG. 11G is a simplified conceptual of cell1132 and its 8 closest neighbouring cells 1140.

In an exemplary implementation, each of the plurality of zones 908 a-908f of FIG. 9 is partitioned into a plurality of contiguous subzones 910a-910 f, respectively, in the form of Geohash cells. As mentioned above,sample vehicle ways may be selected from various locations and thus maybe located at different longitudes. As such, dimensions of Geohash cellsacross a plurality of zones may differ at different longitudes.

In some exemplary implementations, zone dimensions may be dependent ondimensions that are likely to encompass most vehicle ways to be definedusing a classifier. For example, shown in FIG. 10A, is a simplifieddiagram of a plurality of reference areas 1008 a-1008 f of a samedimension defined for sample intersections 802 a-802 f, respectively.Dimensions of reference areas 1008 a-1008 f are approximated toencompass most intersections within an intersection population that maybe defined by a classifier. In this example, reference areas 1008 a-1008f are circular in shape having a radius R. In some instances, areference area may be defined relative to the location of the referencepoint of the vehicle way. For instance, reference areas 1008 a-1008 fare centred about reference points 803 a-803 f of sample intersections802 a-802 f. Accordingly, reference areas 1008 a-1008 f are defined byradius R extending from the reference points 803 a-803 f respectively.

In an exemplary implementation, the inventor determined a reference areadefined radially 25 m from the reference point encompasses mostintersections within an intersection population of interest whilstavoiding extraneous areas.

As noted above, for a plurality of different types and/or subtypes ofvehicle ways a plurality of classifiers may be defined. As such, optimalzone dimensions may vary according to the classifier. One of ordinaryskill will appreciate that an optimal zone dimensions will becomeapparent during definition of the classifier.

FIG. 10B is a simplified conceptual diagram of a plurality of exemplaryzones 1010 a-1010 f imposed on sample intersections 802 a-802 f, eachcomprising a plurality of contiguous subzones 1011 a-1011 f. In thisexample, reference areas 1008 a-1008 f of FIG. 10A are partitioned intoa plurality of contiguous subzones in the form of Geohash cells. FIG.12A is a flow diagram of one exemplary process 1200 for subdividing areference area into a grid of Geohash cells.

Subdividing a reference area into a grid of Geohash cells, process 1200begins at block 1202 wherein a reference point of a sample vehicle wayis mapped to a Geohash cell. For example, LAT/LONG coordinates ofreference point 803 a is input into encode function 1108 and thecorresponding Geohash string is output thereby. Shown in FIG. 12B is asimplified diagram of reference area 1008 a, including reference point803 a and cell 1240, the Geohash cell to which reference point 803 a wasmapped. Geohash cell 1240 serves as a centre cell from which a grid ofcontiguous Geohash cells for subdividing reference area 1008 a isformed.

Next at block 1204, a plurality of Geohash neighbours of the centre cellis determined. For instance, the Geohash string of the Geohash cell 1240is input to Geohash neighbours function 1114, and corresponding stringsof neighbour cells 1242 of Geohash cell 1240 are output. FIG. 12C is asimplified conceptual diagram of Geohash cell 1240 and its 8 closestneighbour cells 1242. Block 1204 repeats a similar step of determiningneighbouring cells of neighbouring cells until reference area 1008 a iscompletely subdivided into a grid of contiguous Geohash cells, as shownin FIG. 12D. Block 1204 may utilize Geohash bounds function 1112 todetermine when to stop process 1200. For example, coordinates for theboundary of reference area 1008 may be input into bounds function 1112to determine which Geohash cells in the grid include the boundary. Oncethe entire boundary is within a Geohash, process 1200 may stop. One ofordinary skill appreciates that there are other methods to manipulate ordivide space using a Geohash system.

Once reference areas are partitioned into a plurality of contiguoussubzones, the associated zone may be defined by peripheral edges of theplurality of contiguous subzones. For instance, once reference areas1008 a-1008 f are defined, they are partitioned into a plurality ofcontiguous subzones 1011 a-1011 f and the peripheral edges 1248 of eachthereof define zones 1010 a-1010 f.

In general, subzones are not limited to a particular dimension, size orshape. However, these attributes may affect processing time andresources for defining and/or using a classification model. Forinstance, higher precision subzones will increase the number of subzonesfor processing in comparison to lower precision subzones for a givenzone.

Embodiments described herein are not limited to partitioning a zone withGeohashes. For example, a zone may be partitioned according to anotherhierarchical geospatial indexing system, e.g., H3—Uber's HexagonalHierarchical Spatial Index, S2—Google's S2 geographic spatial indexingsystem, or other system. Alternatively, a zone may be partitionedaccording to another method for subdividing geographical space.

One of ordinary skill in the art will appreciate that a zone may bedefined in multiple ways. For instance, a plurality of contiguoussubzones may be defined by creating a zone of a shape unique to acorresponding sample vehicle way, as shown in FIG. 9. In anotherinstance, a reference area may be partitioned into a plurality ofcontiguous subzones to create a zone, as shown in FIG. 10B. The methodsfor defining a zone described herein are examples only and are notintended to limit embodiments.

According to some embodiments, for each of the plurality of zones, firstsubzone data may be formed. For each subzone, first subzone data maycomprise information indicating a unique identifier and location (e.g.,geographic coordinates of boundaries) of the subzone. First subzone datamay also comprise information indicating the closest neighbours of thesubzone and/or other subzone related information.

FIG. 13A is a simplified conceptual diagram of exemplary first subzonedata 1302 formed for zone 1010 a. In this example, first subzone data1302 comprises Geohash string data which serves as a unique identifierof each subzone. As described above, the location of a Geohash cell(i.e. subzone) may be determined from a Geohash string, such as byinputting a Geohash string into Geohash system function 1112.

Alternatively, first subzone data may comprise unique ID data whichserves as a unique identifier of each subzone and boundary coordinatesof boundaries thereof, such as LAT/LONG pairs. For example, FIG. 13B isa simplified conceptual diagram of first subzone data 1304 formed forzone 1010 a comprising Geohash string data which serves as a uniqueidentifier of each subzone and LAT/LONG pairs defining boundaries ofeach Geohash.

Optionally, first subzone data may include information indicative of aGeohash cell's 8 closest neighbours.

In some instances, two or more of a plurality of zones may overlap, forexample, zones 1010 d and 1010 f of FIG. 10B. However, in suchinstances, a subzone within more than one zone may skew training and/oroutput of a classification model. For example, vehicle data associatedwith subzones within multiple zones may be overrepresented in trainingand result in a biased classifier.

Block 704

Next, at block 704, a subzone common to multiple zones may be associatedwith a unique zone and then each of the multiple zones is redefined toinclude a new unique plurality of subzones. For example, first subzonedata of the multiple zones are modified in accordance with theredefinition thereof. Block 704 will be described in further detailbelow with reference to FIGS. 14A-14C, FIGS. 15A-15B, and FIGS. 16A-16B.

In an exemplary implementation, the distance between the common subzoneand each of the centre subzones of the multiple zones is calculated. Thecommon subzone is uniquely associated with the zone having a centresubzone that is the shortest distance thereto.

For example, shown in FIG. 14A, is a simplified conceptual diagram ofzones 1010 d and 1010 f comprising centre subzones 1404 and 1406respectively, and a plurality of common subzones located withinoverlapping portion 1402 thereof. FIG. 14B is an enlarged view of aportion of zones 1010 d and 1010 f comprising overlapping portion 1402that includes common subzone 1408. Distance D1 between common subzone1408 and centre subzone 1404 of zone 1010 d is shorter than distance D2between common subzone 1408 and centre subzone 1406 of zone 1010 f, asshown. As such, common subzone 1408 is uniquely associated with zone1010 d and zone 1010 f is redefined to not include common subzone 1408.Each subzone within portion 1402 is analyzed and then uniquelyassociated with one of zones 1010 d and 1010 f followed by theredefinition thereof. FIG. 14C is a simplified conceptual diagram ofredefined zones 1010 d and 1010 f.

FIG. 15A is a simplified conceptual diagram of zone 1010 a comprisingvehicle-position data points 1502 representing positions of vehiclesindicated by raw vehicle data. Now referring to FIG. 15B, shown is anenlarged view of portion 1012 of area 800 imposed on the diagram of FIG.15A. Vehicle-position data points 1502 are present within sampleintersection 802 a, areas 1504 in parking lots 805, portion 1508 of sidestreet 1506, as well as in portions 1510 of non-traffic designated areas807 (e.g., greenspace, sidewalks, as shown. Vehicle-position data pointsfound in portions 1510 may be due to GPS error or other position sensingtechnology error. As described above, a classifier for identifyingsubzones as portions of a vehicle way will be defined based on rawvehicle data associated with the subzones and corresponding zone. FIG.15B illustrates how vehicle traffic may be dispersed in a zone inpractise. However, a classifier may be used to identify only thosesubzones that are occupied by the vehicle way based on raw vehicle dataassociated with the entire zone.

FIG. 16A shows an enlarged view of portion 1012 of area 800 comprisingsample intersection 802 a and paths 1602, 1604 and 1606 of one or morevehicles that have traversed therethrough. A same vehicle may havetraversed sample intersection 802 a at three different time intervals.Alternatively, three unique vehicles may have traversed sampleintersection 802 a. Paths 1602, 1604 and 1606 may have been traversed byany combination of one or more vehicles.

Now referring to FIG. 16B, shown is a conceptual diagram of zone 1010 aand vehicle position-data points 1608 representing position data of rawvehicle data instances that correspond to a position along paths 1602,1604 and 1606. As shown in FIG. 16B, no vehicle-position data pointscorrespond to path 1606. Data collected by a monitoring device wheninside a zone may depend on various factors, such as, the frequency amonitoring device collects operation information, the size of the zone,or other predefined criteria for collecting data, among others. As aresult, there may be instances when a monitoring device collectslittle-to-no data when traversing a zone. Accordingly, there may beoccasions when selecting raw vehicle data based on vehicle position dataindicating a position within a zone may not provide enough meaningfulinformation that relates to all vehicles that have entered the zone. Itwould be advantageous to maximize information available in historicalvehicle data related to vehicles that have entered a zone.

Block 706

Next, at block 706, a subset of raw vehicle data associated with each ofthe plurality of zones is selected from historical vehicle data. In anexemplary implementation, traffic analytics system 104 a may accesshistorical vehicle data, such as historical vehicle data in database209, for selecting the subset stored by traffic analytics system 104 avia communication network 110.

According to an embodiment, the subset of raw vehicle data may beselected based on positions inside a zone. Optionally, the subset of rawvehicle data may be selected based on positions inside and outside thezone. FIG. 16C illustrates vehicle position-data points corresponding topositions within portion 1012 of area 800. Including raw vehicle datacorresponding to positions both inside and outside zone 1010 a in thesubset enables inclusion of raw vehicle data corresponding to thepositions on path 1604, represented by vehicle-position data points1616. This also enables inclusion of more raw vehicle instancescorresponding to paths 1602 and 1606, represented by vehicle-positiondata points 1618.

Block 708

Next, in block 708 interpolating data instances from the subset of rawvehicle data may be performed. For example, FIG. 16D illustratesvehicle-position data points 1621 corresponding to interpolated datainstances. Selecting raw vehicle data corresponding to locations insideand outside a zone, at block 706, and then interpolating data therefromat block 708, may provide more meaningful data for the purpose oftraining a machine learning algorithm in comparison to training based onraw vehicle data instances corresponding to locations inside a zoneonly. Block 708 is described further below with reference to FIGS.16E-16F, FIG. 17, and FIGS. 18A-18B.

Optionally, data instances are interpolated in dependence on thedimensions of subzones of a zone. For example, interpolating data suchthat there is one of an interpolated instance or raw vehicle datainstance corresponding to a position in each subzone along a given pathof a vehicle. Referring to FIG. 16E, shown is a conceptual diagram ofzone 1010 a comprising plurality of subzones 1011 a of less than orequal to 4.77 m×4.77 m. In this example, data instances are interpolatedsuch that there is at least one of an interpolated instance or rawvehicle data instance corresponding to a location in each subzone.

Alternatively, data may be interpolated from raw vehicle datacorresponding to positions only inside a zone. Such as interpolated datainstances corresponding to vehicle-position data points 1614, alongportions 1610 and 1612 of paths 1602 and 1606, as shown in FIG. 16F.

Alternatively, there may be a sufficient amount of meaningful rawvehicle data corresponding to locations inside a zone that selecting rawvehicle data corresponding to locations outside a zone is unnecessary.Alternatively, there may be a sufficient amount of meaningful rawvehicle data in historical vehicle data that interpolation isunnecessary.

According to some embodiments, the subset of raw vehicle data associatedwith a zone comprises raw vehicle data corresponding to positions withina traffic zone. In some instances, a traffic zone may encompass a uniquezone. In other instances, a traffic zone may encompass more than onezone. FIG. 17 is a simplified diagram of an exemplary implementation oftraffic zone 1700 encompassing zones 1010 a-1010 f. For instance,geographic coordinates of boundaries 1702 of traffic zone 1700 areprovided to traffic analytics system 104 a for defining traffic zone1700.

FIG. 18A is a simplified block diagram of process 1800 a for obtainingdata, hereinafter referred to as vehicle data, for generating features.Vehicle data may be indicative of vehicle operating conditions for aplurality of corresponding vehicles. As mentioned above, such data maybe a subset of raw vehicle data selected from historical vehicle data404 that corresponds to a location within a zone and/or traffic zone.For example, boundary data 1802 comprises zone boundary data and/ortraffic zone data indicative of locations of boundaries of a zone and/ora traffic zone, respectively. In block 1804, raw vehicle data isselected from historical vehicle data 404 based on boundary data 1802 toform first vehicle data 1810.

FIG. 18B is a simplified block diagram of an alternative process 1800 bfor obtaining vehicle data for generating features. Such data may be asubset of raw vehicle data selected from historical vehicle data 404that corresponds to a location within a zone and/or traffic zone. Forexample, boundary data 1802 comprises zone boundary data and/or trafficzone data indicative of locations of boundaries of a zone and trafficzone, respectively. In block 1804, raw vehicle data is selected fromhistorical vehicle data 404 based on boundary data 1802. Next, in block1806, data is interpolated from the raw vehicle data selected in block1804 and first vehicle data 1810 comprising the raw vehicle data and thedata interpolated therefrom is formed.

Optionally, raw vehicle data selected from historical vehicle data 404may be selected based on date and time operating conditions are logged.For instance, raw vehicle data corresponding to a particular date and/ortime range is selected. For example, only raw vehicle data collectedover the last 3 months may be selected.

Optionally, at block 1804, the selected data is pre-processed, theselected data is pre-processed, such as, by removing outliers (e.g.,unlikely speed values), duplicate values, and the like.

Block 710

Next, in block 710, features for each of the plurality of subzones ofeach of the plurality of zones are extracted from vehicle data. Featuresof the types described herein may include features that are present in avehicle data instance or a subset of vehicle data instances and/orfeatures derived therefrom. Features present in an instance or a subsetof instances may include numeric values that are explicitly set outtherein. Specific and non-limiting examples of such features include aminimum or maximum numeric value in the subset (where a minimum/maximummay be absolute and/or relative). The minimum or maximum data value mayrequire some analysis, such as a comparison of values, but theminimum/maximum value itself will be a value found within the subset.For instance, a plurality of vehicle data instances in a vehicle datasubset may be analyzed to determine a maximum speed of the subset. Block710 is further described below with reference to FIGS. 19A-19B, FIGS.20A-20E, FIG. 21, FIGS. 22A-22B, FIG. 23, and FIGS. 24A-24B.

Derived features may describe an instance or subset of vehicle datainstances, but include a value not found therein. Instead, a value of aderived feature may be derived from the instance or subset, such asobtained through performing one or more computations on the instance orsubset. Specific and non-limiting examples of derived features includeaverage speed, total number of vehicle visits and ignition ratio.Optionally, a derived feature may describe a first derived featureforming a second derivative of the first derived feature. Additionalderivatives of features may also be possible.

The features may additionally or alternatively be derived from theperformance of one or more statistical computations on a vehicle datasubset. For instance, a derived feature that may be employed may includestandard deviation, mean, and median of values found in a vehicle datasubset. For example, standard deviation, mean, and/or median of speedvalues of vehicles that have traversed a subzone. Features will bedescribed in greater detail below.

The features may be prepared for use in training the model, forinstance, by traffic analytics system 104 a. Preparing the data mayinclude various functions such as removing outliers (e.g., unlikelyspeed values), duplicate values, and the like.

FIG. 19A is a conceptual block diagram of a feature extraction functionfor generating features from vehicle data. In block 1904, a subset ofvehicle data for each of the plurality of subzones of the plurality ofzones may be selected from vehicle data based on first subzone data.Once selected, the subset of vehicle data is analyzed and/orcomputations are performed thereon to extract/generate subzone features1906. For each subzone in each zone, a plurality of features isgenerated. These features may be used to identify patterns in thefeatures by a machine learning algorithm during training for defining aclassifier.

For example, a subset of vehicle data is selected from first vehicledata 1810 based on first subzone data 1902 for each of the plurality ofsubzones of zones 1010 a-1010 f. Once selected, the subset of vehicledata instances is analyzed and/or computations are performed thereon toextract/generate subzone features 1906. For instance, for each Geohashin zones 1010 a-1010 f, a plurality of features, (e.g., F1-Fn) aregenerated. FIG. 19B is an exemplary table 1908 representing datacomprising a plurality of features for each Geohash.

Subzone-Related Features

According to an embodiment, a plurality of subzone-related features isbased on and/or derived from a subset of vehicle data associated witheach subzone.

In a first exemplary implementation, the subset is a first subset ofvehicle data corresponding to a position within a subzone.Subzone-related features indicate measurements/attributes of vehicleoperating conditions of at least one vehicle that has operated in thesubzone.

For example, FIG. 20A is a conceptual diagram of portion 2002 of zone1010 a as demarcated by line 1250A-1250A of FIG. 12D. Portion 2002comprises subzone 2004, as shown. An enlarged view of subzone 2004 isdepicted in FIG. 20B comprising vehicle-position data points 2006 eachthereof indicative of a position of one or more vehicles that haveentered subzone 2004 at one point in time.

Illustrated in FIG. 20C is a simplified functional block diagram of afunction that may be implemented at block 1904. First vehicle data 1810and subzone 2004 first subzone data 2007 is provided in block 2008 and afirst subset of vehicle data corresponding to a position within subzone2004 may be selected. The first subset of vehicle data is represented bythe vehicle-position data points 2006 shown in FIG. 20B. Once selected,the first subset of vehicle data is processed at block 2010. Forinstance, the first subset of vehicle data instances may be analyzedand/or have computations performed thereon at block 2010 to form atleast one feature 2012.

FIG. 20D is a table 2014 representing an example of a first subset ofvehicle data corresponding to a position within subzone 2004. Positiondata 2018 of each instance 2016 is represented by vehicle-position datapoints 2006 as shown in FIG. 20B. Subzone-related features formed fromprocessing the first subset of vehicle data are indicative ofattributes/measurements of vehicle operating data of vehicles withcorresponding device IDs ID1, ID2, ID3, ID4, when operating in subzone2004. These subzone-related features may be used by the ML algorithm toidentify patterns. For descriptive purposes, only 4 vehicles are shownto have entered subzone 2004, in this example. In practice however, thenumber of vehicles that may enter a subzone may be more or less thanfour.

Some specific and non-limiting examples of the subzone-related featuresare provided in Table 1 below.

TABLE 1 Subzone-related Features Minimum vehicle speed Maximum vehiclespeed Average vehicle speed Median vehicle speed Standard deviation ofvehicle speed Minimum ignition Maximum ignition Total number ofignitions on Total number of ignitions off Average number of ignitionsIgnition ratio Total number of vehicle visits Average number ofvisits/vehicle Minimum number of vehicle visits/day Maximum number ofvehicle visits/day Average number of vehicle visits/day Median number ofvehicle visits/day Standard deviation of number of vehicle visits/dayMinimum unique number of vehicle visits/day Maximum unique number ofvehicle visits/day Median unique number of vehicle visits/day Standarddeviation of unique number of vehicle visits/day Average unique numberof visits/day Total number of unique vehicle visits

Other subzone-related features may be based on and/or are derived fromthe first subset of vehicle data instances. Embodiments are not intendedto be limited to the example features described herein. FIG. 20E is atable of exemplary subzone-related features and feature values based onthe subset of vehicle data instances. Other features may be based onand/or be derived from the first subset of vehicle data.

Ignition state indicates whether a vehicle is in a driveable state ornot. For example, an internal combustion engine (ICE) vehicle has anignition state of on when the engine is on. An ICE vehicle has anignition state of off when the engine is off, even if electrical poweris provided to vehicle circuitry by the battery. In another example, anelectric vehicle (EV) has an ignition state of on when electricity isprovided to the EV's electric motor, whereas the ignition state is offwhen no electricity is provided to the EV's electric motor.

The minimum ignition feature of a subzone has a value of 1 only when allvehicles that have entered a subzone have an ignition state of 1. Thismay indicate that the vehicle way is not employed as a parking area.

The maximum ignition feature has a value of 0 only when all vehicles ina subzone have an ignition state of off. This may indicate that thesubzone is a portion of vehicle way employed as a parking area.

The ignition ratio feature is defined as,

${{Ignition}\mspace{14mu}{ratio}} = \frac{{Total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{ignitions}\mspace{14mu}{off}}{\begin{matrix}{( {{Total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{ignitions}\mspace{14mu}{off}} ) +} \\( {{Total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{ignitions}\mspace{14mu}{on}} )\end{matrix}}$

In a second exemplary implementation, subzone-related features may bebased on and/or derived from the first subset of vehicle data and asecond subset of vehicle data including vehicle data temporallypreceding and/or subsequent thereto for a same vehicle.

For instance, the first and second subsets of vehicle data may beprocessed for providing subzone-related features indicative of dwelltime of a vehicle within a given subzone. Shown in FIG. 21 is asimplified diagram of subzone 2004 and vehicle-position data point 2006representing a vehicle position at T1 according to vehicle data. A dwelltime of a vehicle within subzone 2004 may be determined by obtainingvehicle data corresponding to the same vehicle at a preceding point intime, T0, and a subsequent point in time, T2, represented byvehicle-position data points 2106 and 2108 respectively. As geographiccoordinates of boundaries of subzone 2004 are known, the time TA (2110)between the time a vehicle enters subzone 2004 and arrives atvehicle—position data point 2006, and the time TB (2112) between thetime the vehicle leaves vehicle—position data point 2006 and exitssubzone 2004 may be determined. The dwell time, (e.g., how long thevehicle is in a subzone) may be calculated by, T_(DWELL)=TA+TB. For eachvehicle that enters subzone 2004 a dwell time is calculated andfeatures, average dwell time, minimum dwell time, maximum dwell time,median dwell time and standard deviation of dwell time are based thereonor derived therefrom.

In a third exemplary implementation, subzone-related features may bebased on and/or derived from the first subset of vehicle data and athird subset of vehicle data including vehicle data corresponding tovehicle data temporally subsequent thereto for a same vehicle. In thisexample subzone-related features relates to the travel time of a vehiclebetween a location within a subzone and the first location the vehicleignition state is off. In other words, the travel time between aposition within a subzone and position the vehicle parks, i.e., time topark.

FIG. 22A is a simplified diagram of a path of a vehicle that hastraversed subzone 2004 having a related vehicle data instancecorresponding to vehicle-position data point 2006 at time T1, as shown.Each temporally subsequent vehicle data instance is represented by avehicle-position data points 2204 at times T1-T14 each representing anew location of a same vehicle at consecutive points in time as thevehicle travels along path 2200. Vehicle-position data point 2206 at T14represents the first location the vehicle ignition is turned off,indicating that the car has parked. The time to park fromvehicle—position data point 2006 to 2206 may be calculated asT_(TIMETOPARK)=T14-T1. For each vehicle that enters subzone 2004 thetime to park is calculated to provide subzone-related features such as,average time to park, minimum time to park, maximum time to park, mediantime to park, and standard deviation of time to park.

Table 2210 of FIG. 22B represents a third subset of vehicle datacorresponding to vehicle-position data points 2204 at times T1-T14.

In some cases, a vehicle ignition is ON and the speed is 0 km/hr, suchas at T9. For example, when a vehicle is not moving yet the ignition ison. For instance, at red lights, stop signs, heavy traffic(stop-and-go), picking up passengers, going through a drive through,among others.

Zone-Related Features

According to an embodiment, zone-related features may be based on and/orderived from a fourth subset of vehicle data instances associated withthe zone and/or subzone-related features. Table 2 lists specific andnon-limiting examples of such zone-related features. These zone-relatedfeatures may be determined for each subzone of the plurality of subzonesfor each zone of the plurality of zones.

TABLE 2 Zone-Related Features Zone Minimum Ignition OFF Zone MaximumIgnition OFF Zone Average Vehicle Speed Zone Maximum Vehicle Speed ZoneMinimum Vehicle Speed Zone Average Number of Unique Visits/Day ZoneMinimum Number of Unique Visits/Day Zone Maximum Number of UniqueVisits/Day Zone Average Median Number of Unique Visits/Day Zone TotalAverage Number of Unique Visits/Day

In a first exemplary implementation, zone average speed may bedetermined by selecting a subset of vehicle data instances correspondingto a position within a zone and calculating the average speed therefrom.

In a second exemplary implementation, zone average speed may bedetermined by calculating an average of the average speedsubzone-related features of all subzones in a zone.

According to an embodiment, other zone-related features may be based onand/or derived from a fifth subset of vehicle data instances associatedwith the zone. Table 3 lists specific and non-limiting examples of suchzone-related features. These zone-related features may be determined foreach subzone of the plurality of subzones for each zone of the pluralityof zones.

TABLE 3 Zone-Related Features Zone Total Number of Visits Zone TotalNumber of Unique Visits

In an exemplary implementation, zone total number of visits may bedetermined by selecting a subset of vehicle data instances correspondingto a position within a zone and calculating the total number of vehiclesthat correspond to the zone.

According to an embodiment, other zone-related features may be based onand/or derived from another portion of subzone-related features. Table 4lists specific and non-limiting examples of such zone-related features.These zone-related features may be determined for each subzone of theplurality of subzones for each zone of the plurality of zones.

TABLE 4 Zone-Related Features Zone Average Time to Park Zone MaximumTime to Park Zone Minimum Time to Park Zone Maximum Dwell Time ZoneMinimum Dwell Time Zone Median Dwell Time Zone Average Dwell Time ZoneMinimum Number of Unique Visits Zone Average Number of Unique VisitsZone Maximum Number of Unique Visits Zone Average Total Number of VisitsZone Maximum Total Number of Visits Zone Minimum Total Number of Visits

For example, zone average dwell time may be determined by calculating anaverage of the average dwell time subzone-related features of allsubzones in a zone.

Subzone-Zone-Related Features

According to an embodiment, subzone-zone-related features may be basedon and/or derived from a portion of subzone-related features inrelationship to a portion of zone-related features. Subzone-zone-relatedfeatures are determined for each subzone of the plurality of subzonesfor each zone of the plurality of zones.

Specific and non-limiting examples of relationship-based features arelisted in Table 5 below.

TABLE 5 Subzone-Zone-Related Features Minimum Vehicle Speed RatioAverage Vehicle Speed Ratio Maximum Vehicle Speed Ratio Minimum IgnitionOff Ratio Maximum Ignition Off Ratio Maximum Dwell Time Ratio MinimumDwell Time Ratio Average Median Dwell Time Ratio Average Dwell TimeRatio Minimum Time to Park Ratio Average Time to Park Ratio Maximum Timeto Park Ratio Minimum Number of Unique Vehicle Visits Ratio MaximumNumber of Unique Vehicle Visits Ratio Average Number of Unique VehicleVisits Ratio Minimum Unique Number of Vehicle Visits/Day Ratio MaximumUnique Number of Vehicle Visits/Day Ratio Average Unique Number ofVehicle Visits/Day Ratio Total Unique Number of Vehicle Visits/Day RatioAverage Median Unique Number of Vehicle Visits/ Day Ratio Minimum TotalNumber of Vehicle Visits Ratio Maximum Total Number of Vehicle VisitsRatio Average Total Number of Vehicle Visits Ratio Total Number ofVehicle Unique Visits Ratio Total Number of Vehicle Visits Ratio

In an exemplary implementation, subzone-zone-related feature averagespeed ratio may be determined by calculating the ratio ofsubzone-related feature average speed to zone-related feature, zoneaverage speed.

As described above, raw vehicle data may be selected from historicalvehicle data based on a particular date and/or time period. As such,values of features described herein may vary accordingly.

Spatial-Related Features

According to an embodiment, spatial-related features may be based onand/or derived from spatial relationship data of a subzone to the zone.According to an embodiment, spatial-related features may be based onand/or derived from a spatial relationship data of a subzone to theplurality of subzones, or a portion thereof, of a zone.

In a first exemplary implementation, for each subzone, a spatial-relatedfeature may be based on and/or derived from the distance of the subzoneto the centre point of a zone. For instance, one method for determiningthe distance between a subzone and the centre point of a zone comprises,determining a location of a centre point of the subzone and the locationof the centre point of the centre subzone of a zone. Next, the distancetherebetween is calculated. For example, shown in FIG. 23 is asimplified diagram of portion 2002 including vehicle—position data point2006 of subzone 2004, and reference point 803 a, which may be a centrepoint, of zone 1010 a. The distance 2304 between vehicle—position datapoint 2006 (i.e., centre point) and reference point 803 a is determined,for instance, by using the Haversine formula.

In a second exemplary implementation, for each subzone, a feature may bebased on and/or derived from the number of subzones adjacent the subzone(e.g., number of Geohash neighbours). FIG. 24A is a simplifiedconceptual diagram of portion 2002 including subzone 2004 having 8adjacent subzones 2402. FIG. 24B is also a simplified conceptual diagramof portion 2002 including subzone 2404 having 4 adjacent subzones 2406.In these examples, the features for subzones 2004 and 2404 may havevalues 8 and 4 respectively. Alternatively, features for subzones 2004and 2404 may have values be derived therefrom.

In a third exemplary implementation, for each subzone, a feature may bebased on and/or derived from the number of subzones adjacent the subzone(e.g., number of Geohash neighbours) having vehicle data correspondingto a location therein. FIG. 24A shows subzone 2004 having 8 adjacentsubzones 2402. If no vehicle data corresponds to 3 of those adjacentsubzones the value of the feature is 5. In other words, if vehicles didnot enter 3 of the 8 subzones, the number of adjacent subzones havingvehicle data corresponding to a location therein is 5.

Spatial-related features are determined for each subzone of theplurality of subzones for each zone of the plurality of zones.

Example features are not intended to limit embodiments to the featuresdescribed herein.

Block 712

In block 712 training data is formed. For instance, for each subzone,above described features are determined to create training data.Training data further includes an indication of the class of eachsubzone.

Shown in FIG. 25A is a conceptual diagram of a portion of sample vehicleway in the form of sample intersection 802 a and zone 1010 a imposedthereon. Shaded subzones 2502 of zone 1010 a indicate that they areportions of sample intersection 802, whereas the unshaded subzones 2504are not portions of sample intersection. Table 2510 of FIG. 25Brepresents training data including subzone ID, a plurality of featuresgenerated for each associated subzone, and a class label, indicating asubzone is one of a ‘vehicle way’ class, (e.g., a portion of samplevehicle way, such as sample intersection 802 a) or a ‘not-vehicle way’class (e.g., not a portion of a sample vehicle way).

Block 714

Finally, in block 714, a machine learning technique, such as randomforest technique, is implemented using training data to define aclassifier for classifying a subzone as one of a vehicle way class ornot-vehicle way class. The training data used may include all or aportion of the features described herein. Optionally, other features maybe included in the training data.

For example, FIG. 25C is a high-level flow diagram of an exemplaryprocess 2520 for using a machine learning technique to define aclassifier for classifying subzones. Process 2520 begins at block 2522where training data, such as training data represented by table 2510, isinput to a machine learning algorithm. Next, at block 2524, a classifierthat is generated by training the ML algorithm is verified, and at block2526 the performance of the classifier is evaluated. If the classifiermeets performance criteria, (e.g., passes verification) process 2520ends at block 2530. However, if the performance criteria are not met,(e.g., fails verification), the process 2520 continues to block 2528where modifications to training data or machine learning algorithmparameters may be performed and process 2520 continues until the definedclassifier meets the required performance criteria.

Optionally, modifications to other parameters may also be performedshould performance criteria not be met. Such as, the relative positionof the reference areas from the reference points and/or subzonedimensions (e.g., Geohash precision). When performance criteria are met,classifier data associated with the classifier may be generated. Suchclassifier data may be indicative of the relative position of thereference areas from the reference points that were used for definingthe classifier. The classifier data may also be indicative of subzonedimensions (e.g., Geohash precision) of subzones used for defining theclassifier. This classifier data may be useful when using the classifierfor defining a vehicle way. Each classifier may have unique classifierdata associated therewith.

According to some embodiments, a road network may be defined bypartitioning an area into subzones and classifying each thereof as aportion of the road network (i.e., portion of a vehicle way) or not aportion of the road network (i.e., not a portion of a vehicle way) usingat least a machine learning technique. Some embodiments includegenerating unlabelled data including features representing each of thesubzones for classification thereof by a classifier. In some instances,features may be based on and/or derived from vehicle operationinformation of vehicles corresponding to one or more subzones.Aggregating subzones classified as a portion of a road network mayprovide an indication of a geographical location thereof from which aroad network may be defined.

Subprocess 2600

Shown in FIG. 246A is a flow diagram of subprocess 2600 for defining aroad network using a machine learning technique according to anembodiment. Subprocess 2600 is described below as being carried out bytraffic analytics system 104 a. Alternatively, subprocess 2600 may becarried out by telematics system 102, traffic analytics system 104 b,intelligent telematics system 500 a, 500 b, another system, acombination of other systems, subsystems, devices or other suitablemeans provided the operations described herein are performed. Subprocess2600 may be automated, semi-automated and some blocks thereof may bemanually performed.

In an exemplary implementation, traffic analytics system 104 a isconfigured to obtain, store and process second historical vehicle data.For example, traffic analytics system 104 a obtains second historicalvehicle data, for instance, second historical vehicle data 2620, fromtelematics system 102 and stores it in datastore 304 in database 2629.FIG. 26B shows a conceptual diagram of database 2629. In this example,traffic analytics system 104 a organizes second historical vehicle data2620 by vehicle based on associated device ID. For instance, datasets2622-2624 of database 2629 comprise raw vehicle data indicative ofvehicle operation information of vehicles 212-214, respectively.

Shown in FIG. 26C is a conceptual diagram of dataset 2622. In thisexample, each row thereof represents a raw vehicle data instance 2626indicative of vehicle operation information collected by monitoringdevice 202 at different points in time. Raw vehicle data instances 2626of dataset 2622 are organized sequentially in time, from DT10 to DT15.In this example, a raw vehicle data instance 2626 includes device IDdata, speed data, position data, (e.g., LAT/LONG), ignition state data,and date and time data, (e.g., timestamp), as shown.

Block 2602

Subprocess 2600 begins at block 2602 wherein subprocess 2600 includesdefining a road network area comprising a road network of interest. Aroad network area may be defined in a geospatial file (e.g., shape file(.shp), GeoJSON (.geojson)). Alternatively, a road network area may bedefined in another data format indicative of, for example, a polygon,latitude-longitude pairs, GPS coordinates indicating a location of fourcorners bounding the road network area, among others. One of ordinaryskill in the art will appreciate that there are multiple data formatsfor defining geographical coordinates of boundaries of an area.

For instance, data indicating latitude-longitude pairs definingboundaries of a road network area may be provided to traffic analyticssystem 104 a, for example, by a user. Alternatively, traffic analyticssystem 104 a may obtain such data from a governmental agency that storesdata describing geographic coordinates of boundaries of municipalities,communities or other areas of interest, on a publicly accessible server,such as a server accessible via the Internet. Shown in FIG. 27 is asimplified diagram of an exemplary road network area 2700 defined bylatitude-longitude pairs, including latitude pair, (2702, 2704) andlongitude pair, (2706, 2708). In this example, road network area 2700 issquare-shaped, however, a road network area may be any shape and/orsize.

Block 2604

Next, at block 2604, subprocess 2600 includes partitioning the roadnetwork area into a plurality of contiguous second subzones, to define aroad network zone. At this block, subprocess 2600 may generate secondsubzone data, indicative of a second subzone ID of each second subzonein the road network zone. Optionally, second subzone data may include anindication of the boundaries of each second subzone.

In an exemplary implementation, a road network zone may be defined bypartitioning a road network area into a grid of contiguous secondsubzones according to a hierarchical geospatial indexing system.Specific and non-limiting examples of hierarchical geospatial indexingsystems include Geohash, Uber's Hexagonal Hierarchical Spatial Index,i.e., H3, and Google's S2 Geographic Spatial Index, i.e., S2. In anotherexemplary implementation, a road network area may be subdivided into agrid of contiguous second subzones bound by latitude-longitude pairs.Alternatively, a road network area may be partitioned according toanother method for subdividing geographical space. One of ordinary skillappreciates that there are various methods for subdividing ageographical space into a plurality of contiguous second subzones.

Described further below are steps at block 2614 wherein subprocess 2600includes utilizing a classifier for classifying each of the plurality ofcontiguous second subzones in the road network zone. In some instances,classifier data uniquely associated with a classifier utilized at block2614 may indicate a method for partitioning a road network area, e.g., ahierarchical geospatial system, second subzone dimensions and/or otherparameters associated with the classifier.

For example, classifier data associated with a classifier utilized atblock 2614 indicates road network area 2700 is to be partitioned into aplurality of contiguous second subzones according to a Geohashhierarchical geospatial indexing system with a Geohash precision of 9. Aprecision of 9 corresponds to dimensions of Geohashes less than or equalto 4.77 m×4.77 m.

In the present example, traffic analytics system 104 a partitions roadnetwork area 2700 into a plurality of Geohashes 2802 of precision 9forming road network zone 2800 as shown in FIG. 28A. Traffic analyticssystem 104 a also generates second subzone data indicating a secondsubzone ID for each Geohash 2802 in road network zone 2800. FIG. 28Bshows exemplary second subzone data 2804 including a second subzone IDfor each Geohash 2802. In this example, a second subzone ID correspondsto a Geohash string of that particular Geohash. For instance, secondsubzone data 2804 a for Geohash 2802 a includes second subzone ID‘GeohasString1,’ second subzone data 2804 b for Geohash 2802 b includessecond subzone ID ‘GeohashString2’, and so on.

Alternatively, traffic analytics system 104 a generates second subzonedata indicating a second subzone ID for each Geohash 2802 in roadnetwork zone 2800 and a boundary thereof. FIG. 28C shows exemplarysecond subzone data 2806 including a second subzone ID for each Geohash2802 in road network zone 2800. In this example second subzone data 2806includes a second subzone ID corresponding to a Geohash string of thatGeohash, and boundaries thereof. Boundaries of each Geohash 2802 isindicated by a latitude-longitude pair. For instance, second subzonedata 2806 a for Geohash 2802 a includes a second subzone ID‘GeohashString1’ and boundary corresponding to (LAT1, LAT2), (LONG1,LONG2) PAIR1. Second subzone data 2806 b for Geohash 2802 b a secondsubzone ID ‘GeohashString2’ and boundary corresponding to (LAT1, LAT2),(LONG1, LONG2) PAIR2.

Boundaries of a Geohash may be determined by use of a Geohash boundsfunction as described above in reference to FIG. 11D. For example,traffic analytics system 104 a may determine a boundary for each Geohash2802 by implementing a Geohash system bounds function on an associatedGeohash string, i.e., second subzone ID.

Next, subprocess 2600 proceeds to one of block 2605 or block 2606.

Block 2606

At block 2606, subprocess 2600 includes selecting a second subset of rawvehicle data from second historical vehicle data corresponding to theroad network zone.

For example, traffic analytics system 104 a selects a second subset of araw vehicle data from second historical vehicle data 2620 correspondingto road network zone 2800. I.e., a second subset of raw vehicle datahaving position data indicating a position within boundaries of roadnetwork zone 2800 is selected from second historical vehicle data 2620.

Now referring to FIG. 29, shown is a simplified diagram of road networkzone 2800 and exemplary paths 2902, 2904 and 2906, taken by threevehicles that have traversed therethrough. For instance, a same vehiclemay have traversed road network zone 2800 at three different timeintervals. Alternatively, a combination of one or more vehicles may havetraversed road network zone 2800.

Also shown in FIG. 29 are vehicle position-data points 2908 representingposition data of raw vehicle data corresponding to paths 2902, 2904 and2906 within road network zone 2800. Monitoring devices may transmit rawvehicle data intermittently, as illustrated by lack of vehicleposition-data points 2908 on portions 2910 of paths 2902, 2904 and 2906.

Transmission of raw vehicle data by a monitoring device may depend onvarious factors, such as, the frequency a monitoring device collectsvehicle operation information, changes in direction of travel of avehicle, or lack thereof, among others. As such, there may be instanceswhen a monitoring device transmits little raw vehicle data whiletraversing a road network zone.

Block 2610

A method for increasing an amount of data indicative of vehicleoperation within a road network zone may include interpolating data. Forinstance, after block 2606, subprocess 2600 may proceed to block 2610wherein subprocess 2600 includes interpolating data instances from thesecond subset of raw vehicle data selected in block 2606.

In the present example, traffic analytics system 104 a interpolated datainstances from the second subset of raw vehicle data instances selectedfrom second historical vehicle data. For example, shown in FIG. 30 isanother simplified diagram of road network zone 2800, exemplary paths2902, 2904 and 2906, vehicle position-data points 2908, as well asinterpolated vehicle position-data points 3002 interpolated by trafficanalytics system 104 a. FIG. 30 illustrates that interpolating datainstances may increase the amount of data indicative of vehicleoperation information of vehicles corresponding to road network zone2800. In some instances, there may still be portions of paths 2902, 2904and 2906, such as portions 3004, for which no vehicle operationinformation is available.

Optionally, at block 2606 and/or at block 2610, the second subset of rawvehicle data and/or the second subset of raw vehicle data andinterpolated data, interpolated therefrom, is pre-processed. Forexample, by removing outliers (e.g., unlikely speed values), duplicatevalues, and the like.

Another method for increasing an amount of vehicle operation informationrelated to vehicles traversing a road network zone includes selecting asecond subset of raw vehicle data within and beyond boundaries of theroad network zone.

Block 2605

For instance, instead of proceeding to block 2606 after block 2604,subprocess 2600 proceeds to block 2605. At block 2605, subprocess 2600includes defining a second traffic zone larger than, and encompassing,the road network zone for selecting a second subset of raw vehicle datafrom second historical vehicle data. In some instances, a second trafficzone boundary may be based on a predefined distance from boundaries ofthe road network zone.

In the present example, traffic analytics system 104 a defines a secondtraffic zone 3100 based on a predefined parameter, such as 500 m, fromthe boundary of road network zone 2800, as shown in FIG. 31. Secondtraffic zone 3100 encompasses and extends beyond the boundaries of roadnetwork zone 2800, as shown.

According to an embodiment, a second traffic zone may be defined basedon a user configurable parameter. For example, a user provides input totraffic analytics system 104 a, via a user interface thereof, indicatinga distance from the boundary of the road network zone 2800 defining thesecond traffic zone 3100. Alternatively, a user provides a data filecomprising data indicative of the boundary of the second traffic zone.

According to another embodiment, a second traffic zone may be userdefinable. For example, a data file comprising data indicative of theboundary of a second traffic zone 3100 is input to traffic analyticssystem 104 a, via a user interface thereof. Alternatively, a data fileis transmitted to traffic analytics system 104 a, for example, vianetwork interface 306. An exemplary data file includes a geospatial file(e.g., shape file (.shp), GeoJSON (.geojson)).

According to yet another embodiment, a second traffic zone may bedefined based on data retrieved from a remote source. For example,traffic analytics system 104 a may obtain data from a governmentalagency that stores data describing boundaries of the road network area,e.g., geographic coordinates of boundaries of municipalities,communities, on a publicly accessible server, such as a serveraccessible via the Internet. Traffic analytics system 104 a may then adda predefined buffer extending beyond the boundary of road network zone2800 to determine the boundary of second traffic zone 3100. For example,a predefined buffer may be 2 km. In this example, traffic analyticssystem 104 determines the boundaries of second traffic zone 3100 to belocated 2 km from the boundaries of road network zone 2800.

Block 2607

Next at block 2607, subprocess 2600 includes selecting a second subsetof raw vehicle data from second historical vehicle data corresponding tothe second traffic zone.

For example, traffic analytics system 104 a selects a second subset ofraw vehicle data from second historical vehicle data 2620 correspondingto positions within second traffic zone 3100. Referring again to FIG.31, illustrated are vehicle position-data points 3102 representingposition data of the second subset of raw vehicle data selected in block2607 corresponding to paths 2902, 2904 and 2906 within second trafficzone 3100.

Block 2609

Next, at block 2609, subprocess 2600 includes interpolating datainstances from the second subset of raw vehicle data selected in block2607.

In the present example, traffic analytics system 104 a interpolates datainstances from the second subset of raw vehicle data selected fromsecond historical vehicle data 2620 corresponding to second traffic zone3100. FIG. 32 illustrates interpolated vehicle position-data points 3202corresponding to data instances interpolated by traffic analytics system104 a as well as vehicle position-data points 3102 representing positiondata of the second subset of raw vehicle data corresponding to paths2902, 2904 and 2908 within second traffic zone 3100.

Selecting a second subset of raw vehicle data corresponding to locationswithin and beyond a road network zone and interpolating data therefrommay provide more vehicle operation information in comparison to a secondsubset of raw vehicle data corresponding solely to a road network zone.

In some instances, data instances may be interpolated in dependence onthe dimensions of second subzones of a road network zone. For example,data instances may be interpolated such that there is approximately oneof an interpolated instance or raw vehicle data instance correspondingto a position in each second subzone along a path of a vehicle.Alternatively, data instances may be interpolated in dependence onclassifier data. For example, classifier data associated with aclassifier utilized in block 2614 may indicate data instances are to beinterpolated such that there is approximately one of an interpolatedinstance or raw vehicle data instance every 10 m along a path of avehicle.

Optionally, at block 2607 and/or block 2609, the second subset of rawvehicle data and/or the second subset of raw data and interpolated datais pre-processed. For example, by removing outliers (e.g., unlikelyspeed values), duplicate values, and the like.

Optionally, a second subset of raw vehicle data selected from secondhistorical vehicle data may be selected based on date and time vehicleoperating conditions are logged. For instance, the second subset of rawvehicle data corresponds to a particular date and/or time range. Forexample, only raw vehicle data collected over the last 3 months isselected.

Block 2611

Next, at block 2611, subprocess 2600 includes processing second vehicledata for generating features for each of the plurality of contiguoussecond subzones in the road network zone. Second vehicle data includesone of, the second subset of raw vehicle data provided at block 2606,the second subset of raw vehicle data and interpolated data provided atblock 2610, and the second subset of raw vehicle data and interpolateddata provided at block 2609. Second vehicle data includes one or moresecond vehicle data instances corresponding to a second subzone (i.e.,includes position data indicative of a position within a subzone.)Second vehicle data may be preprocessed by removing outliers (e.g.,unlikely speed values), duplicate values, and the like.

Features for each of the plurality of second subzones of the roadnetwork zone are extracted from second vehicle data including at leastone second vehicle data instance. Features of the types described hereinmay include features that are present in a second vehicle data instanceor a subset of second vehicle data instances and/or features derivedtherefrom. Features present in an instance or a subset of instances mayinclude numeric values that are explicitly set out therein. Specific andnon-limiting examples of such features include a minimum or maximumnumeric value in the subset (where a minimum/maximum may be absoluteand/or relative). The minimum or maximum data value may require someanalysis, such as a comparison of values, but the minimum/maximum valueitself will be a value found within the subset. For instance, aplurality of second vehicle data instances in a subset of second vehicledata may be analyzed to determine a maximum speed of the subset.

Derived features may describe an instance or subset of second vehicledata instances, but include a value not found therein. Instead, a valueof a derived feature may be derived from the instance or subset, such asobtained through performing one or more computations on the instance orsubset. Specific and non-limiting examples of derived features includeaverage speed, total number of vehicle visits and ignition ratio.Optionally, a derived feature may describe a first derived featureforming a second derivative of the first derived feature. Additionalderivatives of features may also be possible.

The features may additionally or alternatively be derived from theperformance of one or more statistical computations on a second vehicledata subset. For instance, a derived feature that may be employed mayinclude standard deviation, mean, and median of values found in a secondvehicle data subset. For example, standard deviation, mean, and/ormedian of speed values of vehicles that have traversed a second subzone.Features will be described in greater detail below.

Processing second vehicle data for generating features for each of thesecond subzones in road network zone at block 2611 may includegenerating second subzone-related features, road network zone-relatedfeatures, second subzone-road network zone-related features,spatial-related features, and/or other features. Generation of featuresat block 2611 is described below with reference to FIG. 33 to FIG. 38A

Second Subzone-Related Features

According to an embodiment, a plurality of second subzone-relatedfeatures is based on and/or derived from a subset of second vehicle dataassociated with each second subzone.

In a first exemplary implementation, a plurality of secondsubzone-related features is based on and/or derived from a first subsetof second vehicle data corresponding to a position within a secondsubzone. In this example, second subzone-related features indicatemeasurements/attributes of vehicle operating conditions of at least onevehicle that has operated in the second subzone.

For example, FIG. 33 is a conceptual diagram of second subzone 2802 b ofFIG. 28A. Second subzone 2802 b is shown comprising vehicle-positiondata points 3302 each thereof indicative of a position of one or morevehicles that have entered second subzone 2802 b at one point in time.

Illustrated in FIG. 34A is a simplified functional block diagram of anexemplary process 3400 that may be implemented at block 2611 forgenerating second subzone-related features for a second subzone.

At block 3404, process 3400 includes processing second vehicle data andsecond subzone data corresponding to a second subzone for selecting afirst subset of second vehicle data.

For example, traffic analytics system 104 a provides second vehicledata, 3401, including a second subset of raw vehicle data selected fromsecond historical vehicle data 2620 corresponding to second traffic zone3100 and data interpolated therefrom. Next, traffic analytics system 104a processes second vehicle data 3401 and second subzone data 2806 bcorresponding to second subzone 2802 b. Processing includes selecting afirst subset of second vehicle data corresponding to second subzone 2802b. Vehicle-position data points 3302 are a conceptual representation ofthe first subset of second vehicle data.

At block 3406, process 3400 includes analyzing and/or performingcomputations on the first subset of second vehicle data to form at leastone feature. For example, traffic analytics system 104 a processes thefirst subset of second vehicle data instances to form, for example,feature 3408.

FIG. 34B shows exemplary first subset of second vehicle data 3414corresponding to a position within second subzone 2802 b. Position data3418 of each second vehicle data instance 3416 is represented by avehicle-position data point 3302 as shown in FIG. 33. In this examplesecond subzone-related features formed by processing the first subset ofsecond vehicle data are indicative of attributes/measurements of vehicleoperating data of vehicles while operating in second subzone 2802 b. Forexample, vehicles having corresponding device IDs ID1, ID2, ID3, andID4, For descriptive purposes only 4 vehicles are shown to have enteredsecond subzone 2802 a. In practice however, the number of vehicles thatmay enter a second subzone may be greater or less than four.

Some specific and non-limiting examples of the second subzone-relatedfeatures are provided in Table 6 below.

TABLE 6 Second Subzone-related Features Minimum vehicle speed Maximumvehicle speed Average vehicle speed Median vehicle speed Standarddeviation of vehicle speed Minimum ignition Maximum ignition Totalnumber of ignitions on Total number of ignitions off Average number ofignitions Ignition ratio Total number of vehicle visits Average numberof visits/vehicle Minimum number of vehicle visits/day Maximum number ofvehicle visits/day Average number of vehicle visits/day Median number ofvehicle visits/day Standard deviation of number of vehicle visits/dayMinimum unique number of vehicle visits/day Maximum unique number ofvehicle visits/day Median unique number of vehicle visits/day Standarddeviation of unique number of vehicle visits/day Average unique numberof visits/day Total number of unique vehicle visits

Ignition state, for example ignition state 3419 in second vehicle data3414, indicates whether a vehicle is in a driveable state or not. Forexample, an internal combustion engine (ICE) vehicle has an ignitionstate of on when the engine is on. An ICE vehicle has an ignition stateof off when the engine is off, even if electrical power is provided tovehicle circuitry by the battery. In another example, an electricvehicle (EV) has an ignition state of on when electricity is provided tothe EV's electric motor, whereas the ignition state is off when noelectricity is provided to the EV's electric motor.

A minimum ignition feature of a second subzone has a value of 1 onlywhen all vehicles that have entered a second subzone have an ignitionstate of 1. A minimum ignition feature of 1 may indicate that thevehicle way is not employed as a parking area.

A maximum ignition feature has a value of 0 only when all vehicles in asecond subzone have an ignition state of off. A maximum ignition featureof 0 may indicate that the second subzone is a portion of vehicle wayemployed as a parking area.

The ignition ratio feature is defined as,

${{Ignition}\mspace{14mu}{ratio}} = \frac{{Total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{ignitions}\mspace{14mu}{off}}{\begin{matrix}{( {{Total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{ignitions}\mspace{14mu}{off}} ) +} \\( {{Total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{ignitions}\mspace{14mu}{on}} )\end{matrix}}$

Referring now to FIG. 34C, shown is table 3420 of exemplary secondsubzone-related features and feature values based on the first subset ofsecond vehicle data instances. Other second subzone-related features maybe based on and/or are derived from the first subset of second vehicledata instances. Embodiments are not intended to be limited to theexample features described herein.

In a second exemplary implementation, second subzone-related featuresmay be based on and/or derived from a first subset of second vehicledata and a second subset of second vehicle data corresponding to secondvehicle data temporally preceding and/or subsequent thereto for a samevehicle.

For example, traffic analytics system 104 a selects a first subset ofsecond vehicle data corresponding to second subzone 2802 a. Next,traffic analytics system 104 a selects a second subset of second vehicledata including second vehicle data instances temporally preceding andfollowing second vehicle data instances corresponding to vehicles in thefirst subset of second vehicle data

For instance, shown in FIG. 35 is a simplified diagram of second subzone2802 a and vehicle-position data point 3507 representing a vehicleposition at T1 according to the first subset of second vehicle data. Adwell time of a vehicle within second subzone 2802 a may be determinedby obtaining a second subset of second vehicle data corresponding to thesame vehicle at a preceding point in time, T0, and a subsequent point intime, T2, represented by vehicle-position data points 3506 and 3508respectively. As geographic coordinates of boundaries of second subzone2802 b are known, the time TA (3510) between the time a vehicle enterssecond subzone 2802 a and arrives at point 3507, and the time TB (3512)between the time the vehicle leaves point 3507 and exits second subzone2802 b may be determined. The dwell time, TDWELL, e.g., how long thevehicle is in a second subzone, may be calculated according toTDWELL=TA+TB. For each vehicle that enters second subzone 2802 a,traffic analytics system 104 a calculates a dwell time and processes thedwell time of each of the vehicles to generate features. For example,average dwell time, minimum dwell time, maximum dwell time, median dwelltime and standard deviation of dwell time are based thereon or derivedtherefrom.

In a third exemplary implementation, second subzone-related features maybe based on and/or derived from a first subset of second vehicle dataand a third subset of second vehicle data corresponding to secondvehicle data temporally subsequent thereto for a same vehicle. Forexample, second subzone-related features relate to travel time of avehicle between a location within a second subzone and the firstlocation at which the vehicle ignition state is off. In other words, thetravel time between a position within a second subzone and a positionthe vehicle parks, i.e., time to park.

For example, traffic analytics system 104 a selects a first subset ofsecond vehicle data corresponding to second subzone 2802 a. Next,traffic analytics system 104 a selects a second subset of second vehicledata including second vehicle data instances temporally preceding secondvehicle data instances corresponding to vehicles in the first subset ofsecond vehicle data. Next, traffic analytics system 104 a selects asecond subset of second vehicle data corresponding to the same vehiclesin the first subset on the same path travelled thereby at a later pointin time.

For instance, FIG. 36A shows a simplified diagram of path 3600 of avehicle that has traversed second subzone 2802 b corresponding to thefirst subset of second vehicle data. Vehicle-position data point 3602represents a position of the vehicle at T11 within second subzone 2802b. The second subset of second vehicle data includes a plurality oftemporally subsequent second vehicle data instances represented byvehicle-position data points 3604 at times T11-T24 corresponding to thesame vehicle. Each thereof representing a new location of the samevehicle at consecutive points in time as the vehicle travels along path3600. Vehicle-position data point 3606 at T14 represents the firstlocation the vehicle ignition is turned off, indicating that the car hasparked.

The time to park from position 3602 to 3606 may be calculated asTIMETOPARK=T24−T11. For each vehicle that enters second subzone 2802 b,traffic analytics system 104 a calculates the time to park and generatessecond subzone-related features such as, average time to park, minimumtime to park, maximum time to park, median time to park, and standarddeviation of time to park.

FIG. 36B shows an exemplary third subset of second vehicle data 3610corresponding to vehicle-position data points 3604 at times T11-T24. Insome instances, a vehicle ignition is ON and the speed is 0 km/hr, suchas at T19, corresponding to third subset data instance 3612. Forexample, a vehicle may not be moving yet the ignition is on, e.g., atred lights, stop signs, heavy traffic (stop-and-go), picking uppassengers, going through a drive through, among others.

Road Network Zone-Related Features

According to an embodiment, road network zone-related features may bebased on and/or derived from a fourth subset of second vehicle datainstances associated with the road network zone and/or secondsubzone-related features. Table 7 below lists specific and non-limitingexamples of such road network zone-related features. These road networkzone-related features may be determined for each second subzone of aroad network zone.

TABLE 7 Road Network Zone-Related Features Road Network Zone MinimumIgnition OFF Road Network Zone Maximum Ignition OFF Road Network ZoneAverage Vehicle Speed Road Network Zone Maximum Vehicle Speed RoadNetwork Zone Minimum Vehicle Speed Road Network Zone Average Number ofUnique Visits/Day Road Network Zone Minimum Number of Unique Visits/DayRoad Network Zone Maximum Number of Unique Visits/Day Road Network ZoneAverage Median Number of Unique Visits/Day Road Network Zone TotalAverage Number of Unique Visits/Day

In a first instance, road network zone average speed may be determinedby selecting a fourth subset of second vehicle data instancescorresponding to a road network zone and calculating an average speedtherefrom.

For example, traffic analytics system 104 a selects a fourth set ofsecond vehicle data corresponding to road network zone 2800 andprocesses speed data thereof for determining the average speed of avehicle traversing road network zone 2800.

In a second instance, road network zone average speed may be determinedby calculating an average of the average speed second subzone-relatedfeatures of all second subzones in a road network zone.

For example, traffic analytics system 104 a processes an average speedsecond subzone-related feature associated with each second subzone 2802of road network zone 2800 for determining an average speed thereof.

According to an embodiment, other road network zone-related features maybe based on and/or derived from a fifth subset of second vehicle datainstances associated with the road network zone. Table 8 lists specificand non-limiting examples of such road network zone-related features.These road network zone-related features may be determined for eachsecond subzone of the road network zone.

TABLE 8 Road Network Zone-Related Features Road Network Zone TotalNumber of Visits Road Network Zone Total Number of Unique Visits

In an exemplary implementation, ‘road network zone total number ofvisits’ feature may be determined by selecting a fifth subset of secondvehicle data instances corresponding to a road network zone andcalculating the total number of vehicles that correspond to the roadnetwork zone.

For example, traffic analytics system 104 a selects a fifth subset ofsecond vehicle data instances corresponding to road network zone 2800and processes the fifth subset for determining the total number ofvehicles that have traversed therethrough.

According to an embodiment, other road network zone-related features maybe based on and/or derived from another portion of secondsubzone-related features. Table 9 lists specific and non-limitingexamples of such road network zone-related features. These road networkzone-related features may be determined for each second subzone of theroad network zone.

TABLE 9 Road Network Zone-Related Features Road Network Zone AverageTime to Park Road Network Zone Maximum Time to Park Road Network ZoneMinimum Time to Park Road Network Zone Maximum Dwell Time Road NetworkZone Minimum Dwell Time Road Network Zone Median Dwell Time Road NetworkZone Average Dwell Time Road Network Zone Minimum Number of UniqueVisits Road Network Zone Average Number of Unique Visits Road NetworkZone Maximum Number of Unique Visits Road Network Zone Average TotalNumber of Visits Road Network Zone Maximum Total Number of Visits RoadNetwork Zone Minimum Total Number of Visits

For instance, ‘road network zone average dwell time’ feature may bedetermined by calculating an average of the average dwell time secondsubzone-related features of all second subzones in a road network zone.

For example, traffic analytics system 104 a processes an average dwelltime second subzone-related feature associated with each second subzone2802 of road network zone 2800 for determining an average dwell time forroad network zone 2800.

Second Subzone-Road Network Zone-Related Features

According to an embodiment, second subzone-road network zone-relatedfeatures may be based on and/or derived from a portion of secondsubzone-related features in relationship to a portion of road networkzone-related features. Such relationship-based second subzone-roadnetwork zone-related features are determined for each second subzone ofthe road network zone.

Specific and non-limiting examples of relationship-based features arelisted in Table 10 below.

TABLE 10 Second Subzone-Road Network Zone-Related Features (SSRNZR)SSRNZR Minimum Vehicle Speed Ratio SSRNZR Average Vehicle Speed RatioSSRNZR Maximum Vehicle Speed Ratio SSRNZR Minimum Ignition Off RatioSSRNZR Maximum Ignition Off Ratio SSRNZR Maximum Dwell Time Ratio SSRNZRMinimum Dwell Time Ratio SSRNZR Average Median Dwell Time Ratio SSRNZRAverage Dwell Time Ratio SSRNZR Minimum Time to Park Ratio SSRNZRAverage Time to Park Ratio SSRNZR Maximum Time to Park Ratio SSRNZRMinimum Number of Unique Vehicle Visits Ratio SSRNZR Maximum Number ofUnique Vehicle Visits Ratio SSRNZR Average Number of Unique VehicleVisits Ratio SSRNZR Minimum Unique Number of Vehicle Visits/Day RatioSSRNZR Maximum Unique Number of Vehicle Visits/Day Ratio SSRNZR AverageUnique Number of Vehicle Visits/Day Ratio SSRNZR Total Unique Number ofVehicle Visits/Day Ratio SSRNZR Average Median Unique Number of VehicleVisits/ Day Ratio SSRNZR Minimum Total Number of Vehicle Visits RatioSSRNZR Maximum Total Number of Vehicle Visits Ratio SSRNZR Average TotalNumber of Vehicle Visits Ratio SSRNZR Total Number of Vehicle UniqueVisits Ratio SSRNZR Total Number of Vehicle Visits Ratio

For instance, second subzone-road network zone-related feature averagespeed ratio may be determined by calculating the ratio of secondsubzone-related feature average speed to road network zone-relatedfeature, road network zone average speed.

For example, traffic analytics system 104 a processes secondsubzone-related feature average speed for road network zone 2800 androad network zone-related feature average speed road network zone 2800for determining a ratio thereof for generating a road network zoneaverage speed for road network zone 2800.

Spatial-Related Features

According to an embodiment, spatial-related features may be based onand/or derived from spatial relationship data of a second subzone to theroad network zone. According to an embodiment, spatial-related featuresmay be based on and/or derived from a spatial relationship data of asecond subzone to the plurality of second subzones, or a portionthereof, of a road network zone.

For instance, for each second subzone, a feature may be based on and/orderived from a number of adjacent second subzones adjacent thereto(e.g., the number of neighbours thereof).

Shown in FIG. 37A is a conceptual diagram of a portion 3700 of roadnetwork zone 2800 indicated by A-A in FIG. 28A. Portion 3700 includessecond subzone 3702 having 8 neighbours, second subzones 3704. FIG. 37Bis also a conceptual diagram of the same portion 3700 of road networkzone 2800 including second subzone 2802 a having 4 neighbours, secondsubzone 2802 b and second subzones 3708.

For example, traffic analytics system 104 a processes second subzonedata 2806 and implements a Geohash neighbours function, such as function1114, for determining the number of neighbours of each second subzone2802 of road network zone 2800 that are present therein. For instance,traffic analytics system 104 a determines the value of the number ofneighbours feature of second subzone 3702 is 8 and second subzone 2802 ais 4. Alternatively, spatial-related features for second subzones 3702and 2802 a may have values derived from a number of neighbours feature.

In another instance, for each second subzone, a spatial-related featuremay be based on and/or derived from the number of neighbours of thesecond subzone (e.g., number of Geohash neighbours) having secondvehicle data corresponding to a location therein.

FIG. 37A shows second subzone 3702 having 8 neighbours, second subzones3702. If no second vehicle data corresponds to 3 of those neighbours,the value of the feature is 5. In other words, if vehicles did not enter3 of the 8 second subzones, the number of neighbours having secondvehicle data corresponding to a location therein is 5.

For example, traffic analytics system 104 a processes second subzonedata 2806 and implements a Geohash neighbours function, such as function1114, for determining neighbours of second subzone 3702 of road networkzone 2800. Next, traffic analytics system 104 a processes second vehicledata corresponding to each of the neighbours of second subzone 3702 fordetermining how many thereof have vehicles traversed.

Spatial-related features are determined for each second subzone of theplurality of second subzones for a road network zone. Specific andnon-limiting examples of other spatial-related features include featuresderived from proximity of a second subzone from a traffic light,particular business, and pedestrian area. Example features are notintended to limit embodiments to the features described herein.

FIG. 38A shows a conceptual diagram of exemplary features 3800 generatedfor a second subzone 2802, such as second subzone 2802 b.

According to some embodiments, features generated at block 2611 aregenerated based on classifier data. For instance, classifier data maydefine features needed to ensure accurate classification of secondsubzones by a classifier.

Block 2612

Next, at block 2612, subprocess 2600 includes generating unlabelled dataincluding features generated for each of the plurality of secondsubzones of the road network zone.

For example, shown in FIG. 38B is a conceptual diagram of exemplaryunlabelled data 3802 generated by traffic analytics system 104 a. Eachinstance thereof includes features corresponding to one of each of theplurality of second subzones 2802 in road network zone 2800. Forexample, unlabelled data instance 3802 a includes features correspondingto second subzone 2802 a, unlabelled data instance 3802 b includes tofeatures corresponding to second subzone 2802 b, and so on.

Block 2614

Next, at block 2614, subprocess 2600 includes processing the unlabelleddata by a classifier including providing unlabelled data to theclassifier for classifying each of the plurality of second subzones ofthe road network zone as one of a portion of a vehicle way and not aportion of a vehicle way and generating classification data indicativethereof.

For example, traffic analytics system 104 a implements a classifier 3902shown in FIG. 39A and provides unlabelled data 3802 thereto. Classifier3902 classifies each subzone 2802 and provides classification data 3904indicating the classification of each second subzone 2802 of roadnetwork zone 2800. FIG. 39B is a conceptual diagram of exemplaryclassification data 3904 illustrating each second subzone 2802 has beenassigned a label 3908, i.e., 1 or 0, indicating whether the areacorresponding to a second subzone 2802 is a portion of a vehicle way, ornot a portion of a vehicle way, respectively. Conceptual diagrams ofunlabelled data 3802 and classification data 3904 are provided forexample purposes only and embodiments are not intended to be limited tothe examples described herein.

Now referring to FIG. 40A, shown is another diagram of road network zone2800 illustrating second subzones 2802 labelled as a portion of avehicle way shaded grey. For instance, second subzone 2802 b is labelledas not a portion of a vehicle way and appears white, whereas secondsubzone 2802 j is labelled as a vehicle way and is shaded grey. Parts ofa road network are visually identifiable in FIG. 40A, such as, crossstreets 4002 and 4004, and crescent 4012, among others. An aggregationof the plurality of second subzones 2802 labelled as a portion ofvehicle way indicates a location of road network 4020.

Block 2616

Finally, at block 2616, subprocess 2600 includes defining the roadnetwork.

According to one embodiment, defining the road network includesprocessing classification data for determining each of the plurality ofsecond subzones of a road network zone labelled as a portion of avehicle way, and providing data indicative thereof.

For example, traffic analytics system 104 a processes classificationdata 3904 and generates data indicative of each Geohash labelled as aportion of a vehicle way. Such data indicates the plurality of Geohashesthat defines road network 4020 representing the geographic location ofroad network 4020.

According to another embodiment, defining the road network includesprocessing classification data for determining geographic boundaries ofthe road network and generating data representative thereof. A roadnetwork may be defined by a geospatial file (e.g., shape file (.shp),GeoJSON (.geojson)), or other file format, indicating geographicalcoordinates of boundaries delineating roads forming the road network.Alternatively, a road network may be defined in another data format.

For example, traffic analytics system 104 a processes classificationdata 3904 by converting each of the plurality of Geohashes that definesroad network 4020 (i.e., Geohashes classified as a portion of a vehicleway) into a plurality of polygons. For instance, traffic analyticssystem 104 a converts each Geohash having a label ‘1’ into a firstGeoJSON file, each representing a polygon, for forming a plurality offirst GeoJSON files. Alternatively, traffic analytics system 104 aconverts each Geohash having a label ‘1’ into a single first GeoJSONfile representing a plurality of polygons. Geohashes may be converted,for example, into one or more first GeoJSON files via a Geohash toGeoJSON converter.

Next, traffic analytics system 104 a aggregates the plurality ofpolygons represented by each of the first GeoJSON files to form a secondGeoJSON file. For example, traffic analytics system 104 a may processthe plurality of first GeoJSON files with a postGIS ST_UNION functionthat provides the geometry of the union of each of the plurality ofpolygons to form a second GeoJSON file 4006, shown in FIG. 40B.Alternatively, a single GeoSJON file representing a plurality ofpolygons is processed with a postGIS ST_UNION function for forming asecond GeoJSON file. Second GeoJSON file 4006 indicates geographicalboundaries of road network 4020. An exemplary conceptual diagram of roadnetwork 4020 indicated by second GeoJSON file 4006 is shown in FIG. 40C.

One of ordinary skill in the art will appreciate that there are varioustechniques for defining data indicative of geographical coordinates ofboundaries of a road network.

Classification of second subzones as either a portion of a vehicle way,e.g., 1, or not a portion of a vehicle way, e.g., 0, may result in somesecond subzones to be classified as a false positive or false negative.For instance, referring again to road network zone 2800 in FIG. 40A,shown are incorrectly classified second subzones 4008, i.e., falsepositives, and second subzones 4010, i.e., false negatives.Mislabelling/incorrect classification of second subzones may beattributed to inaccuracy of the classifier used for classifying secondsubzones. Another factor that may contribute to mislabelling of a secondsubzone is that the second subzone is on the borderline of the decisionboundary of the classifier. Furthermore, inaccurate position data due toGPS error may also cause incorrect classification of second subzones.One of ordinary skill in the art will appreciate that mislabelling of asecond subzone may be caused by other factors.

Process 4100

Shown in FIG. 41 is a flow diagram of a process 4100 for relabellingsecond subzones of a road network zone that have been incorrectlyclassified by a classifier. Process 4100 is described in greater detailbelow with reference to FIGS. 42A-42J.

FIGS. 42A, 42D, 42E, 42G, and 42I include conceptual diagrams of asimplified road network zone 4202 comprising second subzones 4203associated with an exemplary road network. In this example secondsubzones 4203 are in the form of Geohashes. A road network zonecomprising 20 second subzones is described in this example forexplanation purposes only and embodiments are not intended to be limitedto the examples described herein. In practise, a road network zone maycomprise any number of second subzones, e.g., hundreds, thousands,millions, and billions.

FIG. 42B shows exemplary classification data 4212 indicating a secondsubzone ID 4213 for each geohash 4203 in road network zone 4202 and alabel/classification 4214 thereof. Each second subzone ID 4213corresponds to a Geohash string of that particular Geohash. Forinstance, second subzone ID 4213 a corresponds to Geohash string‘GeohashString_a’ for Geohash 4203 a, second subzone ID 4213 bcorresponds to Geohash string ‘GeohashString_b’ for Geohash 2802 b, andso on. A label/classification 4214 of each second subzone 4203 of roadnetwork zone 4202 is indicated by the shade thereof, as shown in FIG.42A. For example, second subzone 4203 b is shaded grey indicating aclassification of a portion of a vehicle way, e.g., 1, whereas secondsubzone 4203 a is not shaded grey (i.e., white) indicating aclassification of not a portion of a vehicle way, e.g., 0.

Block 4102

Beginning at block 4102, process 4100 includes determining the number ofsecond subzone neighbours labelled as a portion of a vehicle way foreach second subzone in a road network zone, to form a first neighbourcount.

For instance, traffic analytics system 104 a first determines theneighbours of each second subzone 4203 in road network zone 4202, forexample, by inputting a Geohash string associated with each secondsubzone 4203 of road network zone 4202 into a Geohash neighboursfunction. An exemplary Geohash neighbours function is function 1114described with respect to FIG. 11E. For example, neighbours of secondsubzone 4203 a, having a second subzone ID corresponding to Geohashstring ‘GeohashString_a’, include second subzones 4203 e, 4203 f and4203 b having second subzone IDs corresponding to ‘GeohashString_e’,‘GeohashString_f’, and ‘GeohashString_b’. Traffic analytics system 104 agenerates neighbour data 4215 indicating neighbours of each Geohash 4203in road network zone 4202, as shown in FIG. 42C.

Next, traffic analytics system 104 a processes classification data 4212and neighbours data 4215 associated with each second subzone 4203labelled as a portion of a vehicle way for generating first neighbourcount data. For example, second subzone 4203 b has 1 second subzoneneighbour, second subzone 4203 g, labelled as a portion of a vehicleway, and thus has a first neighbour count of 1. A first neighbour countindicative of first neighbour count data for each second subzone 4203 ofroad network zone 4202 is indicated in FIG. 42D within the respectivesecond subzone. For instance, second subzone 4203 b has a firstneighbour count of 1 and second subzone 4203 g has a first neighbourcount of 3, as shown.

Block 4103

Next, at block 4103, process 4100 includes summing first neighbourcounts of second subzone neighbours labelled as a portion of a vehicleway, for each second subzone labelled as a portion of a vehicle way of aroad network zone, to form a neighbour sum.

For example, traffic analytics system 104 a processes first neighbourcount data for each of second subzones 4203 to form neighbour sum datafor each thereof. For example, second subzone 4203 b has one secondsubzone neighbour labelled as a portion of a vehicle way, second subzone4204 g, having a first neighbour count of 3. The sum of first neighbourcounts of second subzone 4203 b is 3. Traffic analytics system 104 aforms neighbour sum data indicative of a neighbour sum, 3, for secondsubzone 4203 b. In another example, second subzone 4203 g has 3 secondsubzone neighbours labelled as a portion of a vehicle way, secondsubzone 4204 b, second subzone 4204 j, and second subzone 42041, eachhaving a first neighbour count of 1, 3 and 3 respectively. The sum offirst neighbour counts of second subzone 4203 g is a neighbour sum of 7(i.e., 1+3+3). Traffic analytics system 104 a forms neighbour sum dataindicative of a neighbour sum 7 for second subzone 4203 g. A neighboursum indicative of neighbour sum data for each second subzone 4203 isindicated in FIG. 42E within a respective second subzone.

Block 4106

At block 4106, process 4100 includes, for each second subzone labelledas a portion of a vehicle way in a road network zone having one of acorresponding first neighbour count less than 3 or a neighbour sum lessthan 9, relabelling the second subzone as not a portion of a vehicleway.

For example, for each second subzone 4203 in road network zone 4202labelled as a portion of a vehicle way, traffic analytics system 104 aprocesses associated first neighbour count data and associated neighboursum data to determine whether the first neighbour count is less than 3and/or the neighbour sum is less than 9. If an associated firstneighbour count is less than 3 and/or associated neighbour sum data isless than 9, traffic analytics system 104 a modifies the labelcorresponding thereto indicating that the second subzone 4203 is not aportion of a vehicle way.

For instance, first neighbour count for second subzone 4203 b is 1, asindicated in FIG. 42D, which is less than 3. As such, traffic analyticssystem 104 a changes the label associated with second subzone 4203 bfrom 1 to 0. Alternatively, neighbour sum for second subzone 4203 b is3, as indicated in FIG. 42E, which is less than 9. As such, trafficanalytics system 104 a would also change the label associated withsecond subzone 4203 b, from 1 to 0.

In another instance, first neighbour count for second subzone 4203 g is3, as indicated in FIG. 42D, which is not less than 3. As such, trafficanalytics system 104 a does not change the label associated with secondsubzone 4203 g from 1 to 0. However, neighbour sum for second subzone4204 g is 7, as indicated in FIG. 42E, which is less than 9. As such,traffic analytics system 104 a changes the label associated with secondsubzone 4203 g, from 1 to 0.

Traffic analytics system 104 a modifies classification data 4212 to formclassification data 4212′ shown in FIG. 42F. Classification data 4212′is indicative of a present label of each second subzone 4203 of roadnetwork zone 4202. Classification of each second subzone 4203 of roadnetwork zone 4202 based on classification data 4212′ is indicated inFIG. 42G. For example, in FIG. 42G second subzones 4302 b and 4203 g arewhite whereas in FIG. 42E second subzones 4302 b and 4203 g are shadedgrey.

Block 408

Next at block 4108, process 4100 includes determining a number of secondsubzone neighbours labelled as a portion of a vehicle way for eachsecond subzone labelled not a portion of a vehicle way, for forming asecond neighbour count for each thereof.

For instance, traffic analytics system 104 a processes classificationdata 4212′ and neighbours data 4215 associated with each second subzone4203 labelled as not portion of a vehicle way for generating secondneighbour count data. For example, referring again to FIG. 42G, trafficanalytics system 104 a determines second subzone 4203 g has 2 secondsubzone neighbours labelled as a portion of a vehicle way, secondsubzones 4203 j and 42031 and forms second neighbour count dataindicative of a second neighbour count of 2. In another example, trafficanalytics system 104 a determines second subzone 4203 k has 5 secondsubzone neighbours, labelled as a portion of a vehicle way, secondsubzones 4203 j, 42031, 4203 n, 4203 o and 4203 p, and forms secondneighbour count data indicative of a second neighbour count of 5.Traffic analytics system 104 a forms second neighbour count dataindicative of second neighbour counts for each second subzone 4203. Asecond neighbour count for each second subzone 4203 of road network zone4202 is indicated in FIG. 42G within a respective second subzone, asshown.

Block 4110

Next, at block 4110, process 4100 includes relabelling each secondsubzone classified as not a portion of a vehicle way having a secondneighbour count greater than 4 as a portion of a vehicle way.

For example, traffic analytics system 104 a processes second neighbourcount data for each second subzone 4203 of road network zone 4202 todetermine whether a second neighbour count is greater than 4. In thisexample, second subzone 4203 k has a second neighbour count of 5, whichis greater than 4. As such traffic analytics system 104 a modifiesclassification data 4212′ by changing the label of second subzone 4203 kfrom 0 to 1. All other second subzones 4203 in road network zone 4202have a second neighbour count which is not greater than 4.

Next, traffic analytics system 104 a modifies classification data 4212′to form classification data 4212″ shown in FIG. 42H indicative of apresent label of each second subzone 4203 of road network zone 4202.Classification of second subzones 4203 of road network zone 4202 basedon classification data 4212″ is indicated in FIG. 42I. For example,second subzone 4303 k is no longer white and is shaded grey.

Block 4112

At block 4112, process 4100 includes counting the number of secondsubzone neighbours labelled as a portion of a vehicle way for eachsecond subzone labelled as a portion of a vehicle way to form a thirdneighbour count for each thereof.

For example, traffic analytics system 104 a processes classificationdata 4212″ of each second subzone 4203 of road network zone 4202 andneighbour data 4215. Processing includes, for each second subzone 4203labelled as a portion of a vehicle way, determining the number of secondsubzone neighbours thereof also labelled as a portion of a vehicle way,to form third neighbour count data. For instance, second subzone 4203 jhas 3 second subzone neighbours labelled as a portion of a vehicle way,second subzones 4203 m, 4203 o, 4203 k, and thus has a third neighbourcount of 3. Traffic analytics system 104 a forms third neighbour countdata indicative of a third neighbour count for each second subzone 4203in road network zone 4202. A third neighbour count for each secondsubzone 4203 is indicated in FIG. 42I within a respective secondsubzone.

Block 4114

Finally, at block 4114, process 4100 includes, for each second subzonelabelled as a portion of a vehicle way having a third neighbour countless than 3, relabelling the second subzone as not a portion of avehicle way for modifying the classification data.

For example, traffic analytics system 104 a processes third neighbourcount data for each second subzone 4203 of road network zone 4202labelled as a portion of a vehicle way for determining whether a secondsubzone 4203 has a third neighbour count less than 3. In this example,each of second subzones 4203 of road network zone 4202 has a thirdneighbour count 3 or greater. As such traffic analytics system 104 adoes not relabel any second subzone 4203 as not a portion of a vehicleway.

In the present example, none of the second subzones 4203 labelled as aportion of a vehicle way have a third neighbour count less than 3, asshown in FIG. 42I. As such, traffic analytics system 104 a does notrelabel any of the second subzones as not a portion of a vehicle way.However, if a second subzone currently labelled as a portion of avehicle way has a third neighbour count of less than 3, that secondsubzone would be relabelled as not a portion of a vehicle way.

Next, traffic analytics system 104 a modifies classification data 4212″to form classification data 4212′″ shown in FIG. 42J indicative of apresent label of each second subzone 4203 of road network zone 4202. Inthis example, classification data 4212′″ is the same as classificationdata 4212″ as no second subzone labels were modified by trafficanalytics system 104 a at block 4114.

According to an embodiment, subprocess 2600 may proceed to block 2615prior to block 2616 wherein subprocess 2600 includesrelabelling/reclassifying second subzones that were incorrectlyclassified at block 2614 and modifying the classification data toindicate the present classification thereof. For instance, process 4100may be implemented at block 2615 for relabelling second subzones of aroad network zone that have been incorrectly classified at block 2614.

For example, traffic analytics system 104 a implements process 4100 atblock 2615 including processing classification data 3904 at block 4102and modifying classification data 4212′″ to form classified data 4300 atblock 4144, as shown in FIG. 43.

Once second subzones have been relabelled at block 2615, subprocess 2600proceeds to block 2616 including defining the road network based on theclassification data.

For example, referring to FIG. 44A, shown is a conceptual diagram ofroad network zone 2800 illustrating road network 4020′ including secondsubzones 2802 shaded according to classification data 4300. Forinstance, second subzones 2802 labelled as a portion of a vehicle wayare shaded grey and second subzones labelled as not a portion of avehicle way not are shaded in grey (i.e., white.)

Referring again to FIG. 40A, shown are second subzones 4008 that havebeen incorrectly classified (i.e., mislabelled) as a portion of avehicle way, (i.e., false positives). FIG. 40A also shows secondsubzones 4010 that have been incorrectly classified as not a portion ofa vehicle way (i.e., false negatives).

Reclassification of subzones at block 2615 may provide more accurateclassification data that may result in refined boundaries of the definedroad network.

Once second subzones 2802 have been reclassified at block 2615,subprocess 2600 proceeds to block 2616 for defining the road network.

According to one embodiment, defining the road network includesprocessing classification data for determining each second subzone of aroad network zone labelled as a portion of a vehicle way, and providingdata indicative thereof.

For example, traffic analytics system 104 a processes classificationdata 4300 and generates data indicative of each Geohash labelled as aportion of a vehicle way. Such data indicates a plurality of Geohashesthat define road network 4020′ representing the geographic location ofroad network 4020′.

According to another embodiment, defining the road network includesprocessing classification data for determining geographic boundaries ofthe road network and generating data representative thereof. A roadnetwork may be defined by a geospatial file (e.g., shape file (.shp),GeoJSON (.geojson)), or other file format, indicating geographicalcoordinates of boundaries delineating roads forming the road network.Alternatively, a road network may be defined in another data format.

For example, traffic analytics system 104 a processes classificationdata 4300 by converting each of the plurality of Geohashes that definesroad network 4020′ (i.e., Geohashes classified as a portion of a vehicleway) into a plurality of polygons. For instance, traffic analyticssystem 104 a converts each Geohash having a label ‘1’ into a firstGeoJSON file, each representing a polygon, for forming a plurality offirst GeoJSON files. Alternatively, traffic analytics system 104 aconverts each Geohash having a label ‘1’ into a single first GeoJSONfile representing a plurality of polygons. Geohashes may be converted,for example, into one or more first GeoJSON files via a Geohash toGeoJSON converter.

Next, traffic analytics system 104 a aggregates the plurality ofpolygons represented by each of the first GeoJSON files to form a secondGeoJSON file. For example, traffic analytics system 104 a may processthe plurality of first GeoJSON files with a postGIS ST_UNION functionthat provides the geometry of the union of each of the plurality ofpolygons to form the second GeoJSON file 4406, shown in FIG. 44B.Alternatively, a single GeoSJON file representing a plurality ofpolygons is processed with a postGIS ST_UNION function for forming asecond GeoJSON file. Second GeoJSON file 4406 indicates geographicalboundaries of road network 4020′. An exemplary conceptual diagram ofroad network 4020′ indicated by second GeoJSON file 4006 is shown inFIG. 44C.

According to an embodiment, a road network is defined by road networkdata indicative of a first plurality of road network subzonescorresponding to a geographical area occupied by the road network. Eachroad network subzone thereof corresponds to a portion of the roadnetwork. Specific and non-limiting examples of a road network subzoneincludes Geohash—a public domain hierarchical geospatial indexingsystem, H3—Uber's Hexagonal Hierarchical Spatial Index, S2—Google's S2geographic spatial indexing system, or other spatial indexing system.Alternatively, a road network subzone zone may be defined by anothersystem or method for subdividing geographical space.

According to an embodiment, generating road network data defining a roadnetwork includes processing classification data.

For example, traffic analytics system 104 a processes classificationdata 4300 for forming road network data defining road network 4020′.Processing classification data includes selecting a plurality of secondsubzone data 2804 having a label ‘1’ for identifying a subset of theplurality of second subzones that define road network 4020′. Trafficanalytics system 104 a generates road network data indicative of thesubset of the plurality of second subzones defining road network 4020′.

A subzone representing a geographical area occupied by a portion of aroad network is referred to hereinafter as a road network subzone. Inthe present example, the subset of the plurality of second subzones thatdefine road network 4020′ corresponds to a first plurality of roadnetwork subzones.

FIG. 44D is a conceptual diagram of exemplary road network data 4408created by traffic analytics system 104 a. Road network data 4408includes road network subzone location data 4410 indicating a locationof each of the first plurality of road network subzones. Road networkdata 4408 also includes road network subzone ID data 4412 indicating aunique identifier assigned to each second subzone 2802 labelled as aportion of a vehicle way.

In this example, the first plurality of road network subzones are in theform of Geohashes. Referring again to FIG. 44D, road network data 4408includes road network subzone location data 4410 indicating a geohashstring corresponding to a Geohash. For instance, road network datainstance 4408-1 includes road network subzone location data 4410indicating a geohash string, ‘GeohashString_aaa’, corresponding to aGeohash and road network subzone ID 4412 indicating the Geohash has aunique identifier, having a value 001.

According to another embodiment, generating road network data defining aroad network includes partitioning an area occupied by the road networkinto a first plurality of road network subzones. For example, roadnetwork 4020′ is defined in a geospatial file (e.g., shape file (.shp),GeoJSON (.geojson)) provided to traffic analytics system 104 a, forinstance, by the user via a user interface. The geographic area occupiedby the road network may be partitioned according to the Geohashhierarchical geospatial indexing system, Uber's Hexagonal HierarchicalSpatial Index (e.g., H3), Google's S2 geographic spatial indexingsystem, or other spatial indexing system. Alternatively, a road networksubzone may be defined by another system or method for subdividinggeographical space. One of ordinary skill in the art will appreciatethat there are multiple data formats for defining geographicalcoordinates of boundaries of an area.

Alternatively, traffic analytics system 104 a may obtain a geospatialfile from a governmental agency that stores data describing coordinatesof boundaries of public roads of municipalities, counties, states, etc.on a publicly accessible server, such as, a server accessible via theInternet.

According to an embodiment, there is a process for providing trafficmetrics of an intersection of a road network including processing roadnetwork data and third vehicle data corresponding to the intersection.Traffic metrics may include measurements of traffic properties and othermeasurement data relating to vehicles that have traversed anintersection.

Process 4600

Shown in FIG. 46 is a flow diagram of process 4600 for providing trafficmetrics of an intersection.

Process 4600 is described below as being carried out by trafficanalytics system 104 a. Alternatively, process 4600 may be carried outby telematics analytic system 104 b, intelligent telematics system 500a, 500 b, another system, a combination of other systems, subsystems,devices or other suitable means provided the operations described hereinare performed. Process 4600 may be automated, semi-automated and someblocks thereof may be manually performed.

Block 4602

Starting at block 4602, process 4600 includes providing first roadnetwork data including a first plurality of road network subzonesdefining a road network, i.e., a geographical area occupied by a roadnetwork.

For example, traffic analytics system 104 a provides first road networkdata that defines exemplary road network 4500 shown in FIG. 45A. Forinstance, a road agency may store geographic data describing roadnetwork 4500 on a publicly accessible server, such as a serveraccessible via the Internet. The geographic data may be in the form of ageospatial file (e.g., shape file (.shp), GeoJSON (.geojson)), or otherfile format, from which geographical coordinates of boundariesdelineating roads forming the roadway system may be extracted. In thisexample, a geospatial file including boundary coordinates of the roadnetwork way is accessed, and latitude, longitude (Lat/Long) coordinatesof a plurality of points defining the boundaries thereof are extractedfrom the geospatial file.

In this example, a geospatial file including boundary coordinates of theroad network 4500 is received by traffic analytics system 104 a, forinstance, via network interface 306. Traffic analytics system 104 aprocesses the geospatial file and, based thereon, determines a firstplurality of road network subzones 4501, comprising a plurality of roadnetwork subzones 4502, corresponding to the geographical area occupiedby road network 4500, as shown in FIG. 45B. For instance, trafficanalytics system 104 a partitions the geographic area delineated byboundary coordinates of the geospatial file into a plurality of roadnetwork subzones 4502 to form the first plurality of road networksubzones 4501. In this example, traffic analytics system 104 apartitions the geographic area in accordance with the Geohashhierarchical geospatial indexing system. For instance, each road networksubzone 4502 of the first plurality of road network subzones 4501 is inthe form of a Geohash.

Alternatively, the geographic area occupied by the road network may bepartitioned according to Uber's Hexagonal Hierarchical Spatial Index(e.g., H3), Google's S2 geographic spatial indexing system, or otherspatial indexing system. Alternatively, a road network subzone may bedefined by another system or method for subdividing geographical space.One of ordinary skill in the art will appreciate that there are multiplemethods for subdividing geographical space and multiple data formats fordefining geographical coordinates of boundaries of a geographical area.

Next, traffic analytics system 104 a generates first road network data.Shown in FIG. 45C is exemplary first road network data 4510 definingroad network 4500. For each road network subzone 4502 of the firstplurality of road network subzones 4501, first road network data 4510includes road network subzone location data 4512 indicating the locationof the road network subzone and road network subzone ID data indicatinga unique identifier for identifying the road network subzone. In thisexample, road network subzone location data 4512 indicates a Geohashstring indicative of a Geohash.

Alternatively, traffic analytics system 104 a processes classificationdata for forming first road network data 4510 defining road network4500. For example, classification data for road network 4500 may beformed by traffic analytics system 104 a using similar techniquesdescribed hereinabove.

According to an embodiment, a road network includes a plurality of roadnetwork elements. A first specific and non-limiting example of a roadnetwork element includes an intersection formed by two or more roadsintersecting. A second specific and non-limiting example of a roadnetwork element includes a road section including a portion of a roadthat does not intersect another road.

For instance, road network 4500 includes nine intersections 4508A to45081, and sixteen road sections 4504, as shown in FIG. 45A. Roadsections 4504 channel vehicles to and from intersections 4508.

According to an embodiment, an intersection core, also referred toherein as a core, indicates a geographic area corresponding to anintersection. For instance, position data of a third vehicle datainstance corresponding to a location within boundaries of anintersection core indicates that a vehicle is ‘in’, ‘within’, or ‘at’ anintersection. In contrast, position data of a third vehicle datainstance corresponding to a location outside boundaries of anintersection core indicates that a vehicle is not ‘in’, ‘within’, or‘at’ an intersection core.

Intersection cores corresponding to intersections of a road network maybe defined by a subset of road network subzones. Techniques foridentifying a subset of road network subzones forming intersection coresof a road network are described below.

Block 4604

Next, at block 4604, process 4600 includes selecting from the firstplurality of road network subzones a first subset thereof for forming afirst plurality of core subzones and uniquely associating each thereofwith an intersection of the road network. The first plurality of coresubzones define a plurality of intersection cores of the road network.The first plurality of core subzones may include one or more subsetsthereof defining one or more intersection cores.

Process 4600 further includes selecting from the first plurality of roadnetwork subzones a second subset thereof for forming a first pluralityof non-core subzones. The first plurality of non-core subzones defineroad sections of the road network, i.e., geographical area occupied bythe road sections.

According to a first embodiment, process 4600 includes steps describedin process 4700. A flow diagram for process 4700 is shown in FIG. 47A.

Process 4700 Block 4702

Process 4700 begins at block 4702 wherein process 4700 includesobtaining intersection marker data indicating geographic coordinates ofa plurality of intersection markers associated with intersections of theroad network.

A specific and non-limiting example of an intersection marker includestraffic control equipment (e.g., traffic light, traffic lamp, signallight, stop light) for managing traffic of the road network.

In the present example, traffic analytics system 104 a receivesintersection marker data indicating geographic coordinates of trafficcontrol equipment located proximate road network 4500 from a map servervia network interface 306. An exemplary map server includes anOpenStreetMaps© (OSM) server. For instance, traffic analytics system 104a queries an OSM database stored in an OSM server for geographiccoordinates of traffic control equipment within a geographic areacomprising road network 4500.

In this example, traffic analytics system 104 a provides data to the OSMserver indicating geographic coordinates, such as, LAT/LONG coordinates,of vertices 4511 of a polygon indicating boundaries of geographic area4509 comprising road network 4500, as shown in FIG. 45A. Geographiccoordinates of vertices 4511, may be provided to traffic analyticssystem 104 a by a user. For instance, a user may measure locations usinga GPS device, hover over points on a map, such as a map of Google maps,on the perimeter of road network 4500 for obtaining the geographiccoordinates.

Alternatively, boundaries of a geographic area including the roadnetwork may be extracted from a geospatial file (e.g., shape file(.shp), GeoJSON (.geojson)). Such a file may be provided to trafficanalytics system 104 a, for instance, by the user via a user interface.Traffic analytics system 104 a may send the geospatial file to the OSMserver.

Alternatively, a user provides names of a municipality, community, town,etc., comprising road network 4500 to traffic analytics system 104 a.Traffic analytics system 104 a. specifies the name of a municipality,community, town, etc., in a query to an OSM database.

Intersection marker data indicating LAT/LONG coordinates of trafficcontrol equipment within geographic area 4509 is transmitted by the OSMserver and received by traffic analytics system 104 a via communicationnetwork 110 and stored thereby in a datastore, for example, datastore304. Referring now to FIG. 47B, shown is a conceptual diagram 4701 ofgeographic area 4509 including traffic control equipment-position datapoints 4714 representing locations of traffic control equipment withingeographic area 4509. Alternatively, intersection marker data indicatesGPS coordinates of traffic control equipment.

Alternatively, traffic analytics system 104 a queries the OSM databasefor geographic coordinates of all traffic control equipment storedthereby and extracts traffic control equipment data corresponding tolocations within geographic area 4509.

Alternatively, traffic analytics system 104 a obtains traffic controlequipment data from a governmental agency that stores data describinggeographic coordinates of traffic control equipment of municipalities,communities, or other areas of interest, on a publicly accessibleserver, such as a server accessible via the Internet.

Block 4704—Cluster Intersection Marker Locations

Next, at block 4704, process 4700 includes processing intersectionmarker data for clustering intersection markers into groups based ongeography thereof. A group of intersection markers may indicate alocation of an intersection. In some embodiments, processing includesprocessing intersection marker data using a spatial clusteringalgorithm.

For example, traffic analytics system 104 a processes traffic controlequipment data using a spatial clustering algorithm, such as,density-based spatial clustering of applications with noise (DBSCAN).Traffic analytics system 104 a sets DBSCAN parameters ε, epsilon,specifying a distance measure for locating points in the neighborhood ofany point, and minPts, minimum points, specifying a minimum number ofpoints clustered together for a region to be considered dense. Forexample, if there are at least ‘minPts’ points within a radius of ‘ε’ toa point then all of these points are to be considered part of a samegroup. In an exemplary implementation, the inventor determined DBSCANparameter c is defined as 30 m and parameter minPts as 1 providedaccurate grouping of traffic control equipment of most road networks ofinterest. In some instances, traffic at an intersection may becontrolled by only one traffic light.

In this example traffic analytics system 104 a calculates distancebetween LAT/LONG coordinates of intersection markers when processingintersection marker data using DBSCAN. For instance, distance may becalculated using haversine or another using another formula. A person ofskill will appreciate that there are various methods of calculatingsufficiently accurate distance between LAT/LONG coordinates.

Shown in FIG. 47C is a conceptual diagram 4701A of groups of trafficcontrol equipment 4716A, 4716B, 4716C, 4716D, 4716E, 4716F, 4716G,4716H, and 4716I, grouped by traffic analytics system 104 a. Trafficcontrol equipment clustered in a same group may indicate the trafficcontrol equipment manages traffic of a same intersection of the roadnetwork.

Accordingly, LAT/LONG coordinates of traffic control equipment in acluster 4716 represented by traffic control equipment-position datapoints 4714 may provide an indication of a location of an intersection.For example, traffic control equipment-position data points 4714-1,4714-2, 4714-3, and 4714-4, in cluster 4716A may indicate correspondingtraffic control equipment manages traffic of a same intersection.

Block 4706

Next at block 4706, process 4700 includes processing intersection markerdata for determining an intersection reference point associated witheach group. An intersection reference point indicates a general area anintersection of the road network may be located. In some instances, anintersection reference point is a central location between clusteredtraffic control equipment.

For example, traffic analytics system 104 a processes traffic controlequipment data indicating LAT/LONG coordinates of corresponding trafficcontrol equipment represented by traffic control equipment-position datapoints 4714-1, 4714-2, 4714-3, and 4714-4 in group 4716A, fordetermining a centroid thereof. Shown in FIG. 47D is another conceptualdiagram 4701B of groups of traffic control equipment illustratingcentroid-position data point 4718A representing the centroid of trafficcontrol equipment in cluster 4716A as determined by traffic analyticssystem 104 a. A person of skill will appreciate there are various waysto calculate a geographic centre between coordinates.

Traffic analytics system 104 a processes remaining traffic controlequipment data to determine centroids 4718B, 4718C, 4718D, 4718E, 4718F,4718G, 4718H, and 4718I, of groups 4716B, 4716C, 4716D, 4716E, 4716F,4716G, 4716H, and 4716I, respectively, as shown in FIG. 47D.

Block 4708

Next at block 4708, process 4700 includes defining intersectionreference area(s) based on locations of intersection reference points.

Shown in FIG. 47E is a conceptual diagram of the first plurality of roadnetwork subzones 4502 and intersection reference areas 4722 andintersection reference points 4718 superposed thereon;

In this example, traffic analytics system 104 a defines intersectionreference areas 4722 having a boundary at a radius, R1 4720, fromintersection reference points 4718.

Dimensions of an intersection reference area may be approximated toencompass most intersection cores within an intersection population. Inan exemplary implementation, the inventor determined an intersectionreference area defined radially 25 m from the intersection referencepoint encompasses most intersection cores within an intersectionpopulation of interest whilst avoiding extraneous areas.

In the present example, traffic analytics system 104 a createsintersection reference area data according to a geospatial data modelfor indicating a geographic location of intersection reference areas4722. For instance, intersection reference area data indicatingboundaries of a plurality of circles representing the locationintersection reference area(s). Alternatively, intersection referencearea data represents a plurality of polygons.

Alternatively, intersection reference area data may represent geographiclocation of the intersection reference areas in another data format. Forexample, a polygon in proprietary format, shape file (.shp), GeoJSON(.geojson) format, and the like.

Block 4710

Finally, at block 4710, process 4700 includes processing first roadnetwork data and intersection reference area data for identifying afirst subset of road network subzones located within each intersectionreference area corresponding to a first plurality of core subzones andmapping each thereof to an intersection. Process 4700 further includesdefining an intersection core of each intersection of the road networkrepresenting a geographical area occupied thereby. Defining anintersection core includes uniquely mapping each core subzone of thefirst plurality of core subzones overlapping an intersection referencearea to a corresponding intersection. A subset of the first plurality ofcore subzones uniquely mapped to an intersection defines theintersection core.

Finally, process 4700 also includes identifying a second subset of roadnetwork subzones not located within intersection reference areas thatcorrespond to a first plurality of non-core subzones.

According to an embodiment, identifying a road network subzone as one ofa core and non-core subzone includes determining whether the roadnetwork subzones overlaps an intersection reference area.

Process 4740

Shown in FIG. 47F is a flow diagram of an exemplary process 4740 fordetermining whether a road network subzones overlaps an intersectionreference area for labelling the road network subzone as one of a coreand non-core subzone.

For example, traffic analytics system 104 a processes first road networkdata 4510 and intersection reference area data for determining whethereach road network subzone 4802 of the first plurality of road networksubzones 4801 overlaps an intersection reference area 4722, labels eachroad network subzone 4802 as a core or non-core subzone and maps eachcore subzones of the first plurality of core subzones to anintersection.

At block 4742 of process 4740, determines geographic coordinates of acentre point of each road network subzone.

For example, traffic analytics system 104 a determines geographiccoordinates of a centre of each road network subzone 4502 of the firstplurality of road network subzones 4501, by using, for example, aGeohash decode function. For example, shown in FIG. 47G is a simpleblock diagram of exemplary Geohash decode function 4760 for resolving aGeohash string to a centre location of the corresponding Geohash. Foreach road network subzone ID 4514 of first road network data 4510,traffic analytics system 104 a inputs road network subzone location data4512 indicative of a Geohash string into decode function 4760 whichoutputs LAT/LONG coordinates of a centre of a corresponding Geohash.Alternatively, decode function 4760 which outputs GPS coordinates of acentre of a corresponding Geohash.

For instance, road network subzone location data 4512-2 indicating‘GeohashString-002’ is input into the decode function 4760. Decodefunction 4760 outputs LAT/LONG coordinates of centre point 4732 (i.e.,centroid) of Geohash 4502-002, as shown in FIG. 47E.

Next, at block 4744 for each road network subzone 4502 (i.e., Geohash)of the first plurality of road network subzones 4501, traffic analyticssystem 104 a evaluates whether a centre point of the road networksubzone 4502 is within an intersection reference area 4722.

If a centre point of a road network subzone 4502 is outside allintersection reference areas 4722, the road network subzone 4502 isidentified as a non-core subzone. If all the centre point of a roadnetwork subzone 4502 is found within an intersection reference area4722, the road network subzone 4502 is identified as a core subzone.

For example, centre point 4732 of Geohash 4502-002 is outside allintersection reference areas 4722, thus Geohash 4502-002 is identifiedas a non-core subzone.

In another example, centre point 4737 of Geohash 4502-700 is within theboundaries of intersection reference area 47221, thus Geohash 4502-700is labelled as a core subzone. Next, as Geohash 4502-700 overlaps withintersection reference area 47221 traffic analytics system maps Geohash4502-700 to intersection 47221, or intersection I.

For each road network subzone 4502, traffic analytics system 104 adetermines whether the road network subzone is or is not located withinintersection reference areas 4722, using the process described above.

Alternatively, other points of a road network subzone are evaluated fordetermining whether it overlaps an intersection reference pointincludes: any point of a road network subzone overlaps, all verticesoverap, a predefined portion of vertices overlap, for example, at least2 vertices must overlap, a minimum predefined portion of a road networksubzone area of a road network overlaps, a minimum of 50% of a roadnetwork subzone's area overlaps with an intersection reference are forexample, or any combination thereof.

Shown in FIG. 48 is another conceptual diagram of the first plurality ofroad network subzones 4501 including a first plurality of core subzones4801 and the first plurality of non-core subzones 4803 identified bytraffic analytics system 104 a. Also shown in FIG. 48 are intersectioncores 4804, each including a subset of the first plurality of coresubzones 4801 and uniquely associated with a corresponding intersection.

For example, core subzones 4801A of intersection core 4804A are uniquelyassociated with intersection 4802A. Similarly, each core subzone 4801 ofintersection cores 4804B, 4804C, 4804D, 4804D, 4804E, 4804F, 4804G,4804H, and 4802I is uniquely associated with intersection 4802B, 4802C,4802D, 4802D, 4802E, 4802F, 4802G, 4802H, and 4802I, respectively. Forease of description, intersections 4802A, 4802B, 4802C, 4802D, 4802D,4802E, 4802F, 4802G, 4802H, and 4802I, are also referred to herein asintersections A, B, C, D, E, F, G, H, and I. For clarity, the letters,A, B, C, D, E, F, G, H and I, are superposed on intersection cores4804A, 4804B, 4804C, 4804D, 4804D, 4804E, 4804F, 4804G, 4804H, and 4802Irespectively.

FIG. 48 also illustrates a plurality of road sections 4806-1 to 4806-15and the first plurality of non-core subzones 4803. The first pluralityof non-core subzones 4803 represents a geographic location(s) of theplurality of road sections 4806 of road network 4500.

Block 4606—Modify First Road Network Data

Returning now to process 4600, at block 4606 process 4600 includesmodifying first road network data to include road network subzone labeldata and intersection mapping data indicating a road network subzone isa core subzone or a non-core subzone, and an intersection to which theroad network subzone is mapped, respectively.

Traffic analytics system 104 a generates modified first road networkdata by adding road network subzone label data and intersection mappingdata to road network data 4510. Exemplary first road network data 4900modified by traffic analytics system 104 a is shown in FIGS. 49A, 49Band 49C. First road network data 4900 includes road network subzone IDdata 4514, road network subzone location data 4512, road network subzonelabel data 4902 indicating the subzone is a core subzone or non-coresubzone, and intersection mapping data 4904 indicating anintersection(s) to which a road network subzone is mapped, if any.

For example, modified first road network data instance 4900-2 includesroad network subzone location data 4512 indicating a Geohash string,GeohashString-002, which identifies a location of road network subzone4502-002 shown in FIG. 48. First road network data instance 4900-2 alsoincludes road network subzone label data 4902 indicating road networksubzone 4502-002 is a non-core subzone. Road network instance 4900-2also includes intersection mapping data 4904 indicating that roadnetwork subzone 4502-002 is not presently associated with anintersection, and road network subzone ID data indicating road networkidentifier of 002. Road network subzone 4502-002 is a non-core subzoneof road section 4806-1, as shown in FIG. 48 and FIG. 49A.

In a second example, road network instance 4900-144 includes roadnetwork subzone location data 4512 indicating a Geohash string,GeohashString-144, identifying a location of road network subzone4502-144, as shown in FIG. 48. First road network data instance 4900-144also includes road network subzone label data 4902 indicating roadnetwork subzone 4502-144 is a core subzone. Road network instance4900-144 also includes intersection mapping data 4904 indicating thatroad network subzone 4502-144 is mapped to intersection 4802B.

Block 4608

Next, at block 4608, process 4600 includes mapping each non-core subzoneof the first plurality of non-core subzones with at least anintersection of the road network. Firstly, process 4600 includes formingrepresentative point data for each road network subzone of the firstplurality of road network subzones of the road network. Representativepoint data indicates a location (e.g., LAT/LONG coordinates, GPScoordinates) representing a location of a road network subzone.

Next, process 4600 includes, for each intersection core of the roadnetwork, processing representative point data for a subset of coresubzones corresponding to an thereto and processing representative pointdata of the first plurality of non-core subzones, for clusteringrepresentative points into groups based on geography thereof. In someembodiments, processing includes processing representative point datausing a spatial clustering algorithm.

Finally, process 4600 includes mapping each non-core subzone of thefirst plurality of non-core subzones with one or more intersectionsdependent on clustering of a corresponding representative point in asame group with a subset of core subzones corresponding to anintersection core. In some instances, a non-core subzone is associatedwith more than one intersection.

For example, traffic analytics system 104 a determines a representativepoint representing a location of each road network subzone 4502 of thefirst plurality of road network subzones 4501 of road network 4500. Forinstance, traffic analytics system 104 a processes road network subzonelocation data 4512 of first road network data 4900 representing aGeohash string with a Geohash decode function, such as decode function1110 of FIG. 11C. Function 1110 resolves Geohash strings 4512 to acentre point of a corresponding Geohash 4502. In this example, function1110 defines a location, e.g., LAT/LONG coordinates, of a centre pointof a Geohash. In this example, the location of the centre point of theGeohash represents the Geohash's location.

FIG. 50A is a conceptual diagram of exemplary representative points 5001determined by traffic analytics system 104 a superposed on respectiveroad network subzones 4502 of the first plurality of road networksubzones. FIG. 50A also shows the first plurality of non-core subzones4803 and the first plurality of core subzones 4801.

Next, traffic analytics system 104 a processes representative point datafor representative points 5001 of the first plurality of non-coresubzones 4803 and a subset of the first plurality of core subzones 4801of intersection core 4804A with a spatial-clustering algorithm, such as,density-based spatial clustering of applications with noise (DBSCAN).Traffic analytics system 104 a sets DBSCAN parameters £, epsilon,specifying a distance measure for locating points in the neighborhood ofany point, and minPts, minimum points, specifying a minimum number ofpoints clustered together for a region to be considered dense. Forexample, if there are at least ‘minPts’ points within a radius of ‘£’ toa point then all of these points are to be considered part of a samegroup. In this example, Geohashes 4802 have precision 9 and are 4.77m×4.77 m in size. Traffic analytics system 104 a sets DB SCAN parameterε, as 5 an exemplary implementation, the inventor determined DBSCANparameter ε is defined as 5 m to evaluate any adjacent geohashes andparameter minPts as 1. In this example traffic analytics system 104 acalculates distance between representing points when implementingDBSCAN, by implementing, for instance, haversine. Alternatively, otherformulas are used. A POS will appreciate that there are various methodsof calculating accurate distance between lat/lon coordinates.

Alternatively, traffic analytics system 104 a processes representativepoint data for representative points 5001 of the first plurality ofnon-core subzones 4803 and a subset of the first plurality of coresubzones 4801 of intersection core 4804A for clustering road networksubzones 4502 into groups, such as, performing recursive adjacencychecks.

Representative points 5001 indicate locations of road network subzones4502, as such, DBSCAN evaluates relative distance therebetween forclustering representative points 5001 into groups.

FIG. 50A illustrates a conceptual diagram of representative points 5001superposed on corresponding road network subzones 4502 of the firstplurality of road network subzones 4501. FIG. 50A also shows the firstplurality of non-core subzones 4803 and the first plurality of coresubzones 4801, including core subzones 4801A, 4801B, 4801C, 4801D,4801E, 4801F, 4801G, 4801H, and 4801I corresponding to cores 4804A,4804B, 4804C, 4804D, 4804E, 4804F, 4804G, 4804H, and 4804I,respectively.

Next, traffic analytics system 104 a clusters representative points 5001corresponding to the first plurality of non-core subzones 4803 and coresubzones 4801A of core 4804A including processing correspondingrepresentative point data using DB SCAN. In this instance,representative points 5001 corresponding to core subzones of cores4804B, 4804C, 4804D, 4804E, 4804F, 4804G, 4804H, and 4804I are notprocessed by traffic analytics system 104 a.

FIG. 50B shows exemplary groups 5002 of representative points 5001 asgrouped by traffic analytics system 104 a. Representative points 5001for non-core subzones 4803 of road sections 4806-1, 4806-2, 4806-14 and4806-16 are clustered in the same group 5002-1, with representativepoints 5001 of core subzones 4801A of core 4804A, as shown. As such,traffic analytics system 104 a maps each non-core subzone 4803 of roadsections 4806-1, 4806-2, 4806-14 and 4806-16 with intersection 4802A.

Next, traffic analytics system 104 a clusters representative points 5001corresponding to the first plurality of non-core subzones 4803 and coresubzones 4801B of core 4804B in a similar manner as described above.

FIG. 50C illustrates a conceptual diagram of representative points 5001corresponding to the first plurality of non-core subzones 4803 and coresubzones 4801B of core 4804B superposed on corresponding road networksubzones 4502. In this instance, representative points 5001corresponding to core subzones of cores 4804A, 4804C, 4804D, 4804E,4804F, 4804G, 4804H, and 4804I are not processed by traffic analyticssystem 104 a.

FIG. 50C further shows group 5004 as determined by traffic analyticssystem 104 a. Representative points 5001 for non-core subzones 4803 ofroad sections 4806-2, 4806-3 and 4806-4 are clustered in the same group5004 with representative points 5001 of core subzones 4801B of core4804B, as shown. As such, traffic analytics system 104 a associates eachnon-core subzone 4803 of road sections 4806-2, 4806-3 and 4806-4 withintersection 4802B.

In the present example, representative points 5001 corresponding tonon-core subzones 4803 of road sections 4806-2 are clustered withrepresentative points 5001 corresponding to core subzones 4801 of core4804A in group 5002A and with representative points 5001 correspondingto core subzones 4801 of core 4804B in cluster 5004. As such, non-coresubzones 4803 of road sections 4806-2 are associated with twointersections, namely with intersections 4802A and 4802B.

Next, traffic analytics system 104 a clusters representative points 5001corresponding to the first plurality of non-core subzones 4803 and coresubzones 4801 of cores 4804C, 4804D, 4804E, 4804F, 4804G, 4804H, and4804I, respectively, in a same manner as described above.

FIGS. 50D and 50E are exemplary conceptual diagrams of representativepoints 5001 corresponding to the first plurality of non-core subzones4803 and core subzones of cores 4804C and 4804G, respectively,superimposed on corresponding road network subzones 4502.

FIG. 50D also shows group 5006 as determined by traffic analytics system104 a. Representative points 5001 for non-core subzones 4803 of roadsections 4806-3, 4806-4, and 4806-5 are clustered in the same group 5006with representative points 5001 of core subzones 4801 of core 4804C, asshown.

FIG. 50E also shows group 5008 as determined by traffic analytics system104 a. Representative points 5001 for non-core subzones 4803 of roadsections 4806-3, 4806-4, and 4806-5 are clustered in the same group 5008with representative points 5001 of core subzones 4801 of core 4804G, asshown.

Finally, traffic analytics system 104 a maps non-core subzones 4803 withintersections based on representative points 5001 of the non-coresubzones 4803 being grouped in a same cluster as representative points5001 of core subzones 4803 of one or more of cores 4804C, 4804D, 4804E,4804F, 4804G, 4804H, and 4804I.

Table 12 below provides a list of road sections 4806 and intersection(s)to which non-core subzones 4803 are mapped by traffic analytics system104 a.

TABLE 12 ROAD SECTION INTERSECTION(S) 4806-1  4802A 4806-2  4802A, 4802B4806-3  4802B, 4802C 4806-4  4802B, 4802C 4806-5  4802C, 4802D 4806-6 4802D 4806-7  4802D, 4802E 4806-8  4802E 4806-9  4802E, 4802F 4806-104802G, 4802F 4806-11 4802F 4806-12 4802G, 4802F 4806-13 4802H, 4802I4806-14 4802A, 4802B 4806-15 4802I 4806-16 4802A

Referring now to FIG. 51, shown is another conceptual diagram of thefirst plurality of road network subzones 4501 indicating mapping ofnon-core subzones 4803 of road sections 4806-1 to 4806-16 tointersections 4802A to 4802I. For example, non-core subzones 4803 ofroad section 4806-7 are mapped to intersections 4802D and 4802E, asshown. In another example, non-core subzones 4803 of road section4806-13 are mapped to intersections 4802H and 4802I, as shown.

Block 4610

Next, at block 4610, process 4600 includes creating second road networkdata for each road network subzone of the first plurality of roadnetwork subzones.

Exemplary second road network data 5200 created by traffic analyticssystem 104 a is shown in FIGS. 52A, 52B, and 52C. Second road networkdata 5200 includes road network subzone ID data 4514, road networklocation subzone data 4512, road network subzone label data 4902, andintersection mapping data 5202. Traffic analytics system 104 a modifiesintersection mapping data 4904 of first road network data 4500 toinclude intersection mapping information for road network subzoneshaving a label as non-core, as shown.

Block 4612

At block 4612, process 4600 includes creating journey data for aplurality of vehicles wherein journey data is indicative of a vehiclejourney. Creating journey data includes selecting at least a firstsubset of temporally consecutive vehicle data instances from thirdvehicle data.

Third vehicle data may include raw vehicle data collected over a periodof time (i.e, historical vehicle data), for example, historical vehicledata 404, and/or raw vehicle data collected over a period of time andvehicle data interpolated therefrom, such as, secondary historicalvehicle data 2620. Third vehicle data includes temporally consecutivevehicle data instances indicative of vehicle operation over a period oftime.

Specific and non-limiting examples of third vehicle data include, deviceID data, position data indicating a location (e.g., LAT LONGcoordinates), Ignition state data indicating whether the ignition is ONor OFF, DateTime data indicating the date and time vehicle data waslogged by a monitoring device, speed data indicating speed of vehicle,VIN data indicating a Vehicle Identification Number, turn indicatorstate data indicating whether the turn indicator is ON or OFF, and thedirection indicated is left or right. One of ordinary skill in the artwill appreciate that raw vehicle data may comprise data indicative ofnumerous other vehicle operating conditions.

In some instances journey data may include all third vehicle data. Forexample, a first third vehicle data includes, device ID data, positiondata, Ignition state data, DateTime data speed data VIN data, turnindicator state data. A third vehicle data instance is selected fromthird vehicle data for forming a journey data instance. The journeyinstance includes device ID data, position data, Ignition state data,DateTime data speed data VIN data, turn indicator state data.Alternatively, the journey data instance includes a subset of device IDdata, position data, Ignition state data, DateTime data speed data VINdata, turn indicator state data.

Referring now to FIG. 53A, shown is another simplified block diagram oftraffic analytics system 104 a including processing resource 302,datastore 304, and network interface 306.

In a first example, traffic analytics system 104 receives historicalvehicle data 5302 from remote system 106 via communication network 110and stores historical vehicle data 5302 in a database 5300 in datastore304. In this example, third vehicle data includes historical vehicledata 5302.

In a second example, traffic analytics system 104 processes historicalvehicle data 5302 for forming interpolated data therefrom. Trafficanalytics system 104 forms third vehicle data 5306 including historicalvehicle data 5302 and interpolated data, and stores third vehicle data5306 in database 5304 in datastore 304.

In some instances, third vehicle data instances may be interpolated independence on the dimensions of road network subzones defining a roadnetwork. For example, third vehicle data instances may be interpolatedsuch that there is approximately one of an interpolated instance or rawvehicle data instance corresponding to a location in each road networksubzone.

For example, road network subzones 4802 of the first plurality of roadnetwork subzones are in the form of Geohashes having approximatedimension 4.77 m×4.77 m. In this example, third vehicle data instancesare interpolated such that there is at least one of an interpolatedinstance or raw vehicle data instance corresponding to a location ineach road network subzone along a vehicle's path.

Alternatively, vehicle data instances may be interpolated based on aminimum distance between vehicle positions indicated by consecutivethird vehicle data instances. A specific and non-limiting example of aminimum distance includes 10 m. For example, third vehicle datainstances are interpolated such that a distance of no more than 10 m isexceeded between vehicle positions indicated by consecutive thirdvehicle data instances.

In some instances, traffic analytics system 104 receives raw vehicledata from monitoring devices of one or more vehicle fleets and collectsthe raw vehicle data to form historical data. Third vehicle dataincludes this historical data and/or historical data and vehicle datainterpolated therefrom.

Now referring to FIG. 53B, shown is a conceptual diagram of thirdvehicle data 5306 organized by device ID. For instance, vehicle-specificdatasets 5311, 5312 and 5313 include third vehicle data instances ofthird vehicle data 5306 corresponding to device ID111, device ID112, anddevice ID113, respectively. Third vehicle data may be organized innumerous manners.

FIG. 53B also illustrates journey data 5334, 5335 and 5536 selected fromthird vehicle data 5306 by traffic analytics network 104 a. Forinstance, journey data 5334, 5335 and 5536 are selected fromvehicle-specific datasets 5311, 5312 and 5313, respectively.

For ease of description only three vehicle-specific datasets, 5311, 5312and 5313, corresponding to three unique monitoring devices selected fromthird vehicle data 5306 are described in this example. In practise,however, third vehicle data may include vehicle data from any number ofmonitoring devices and thus include any number of vehicle-specificdatasets. In one example, raw vehicle data is received by trafficanalytics network 104 a from hundreds devices, resulting in thirdvehicle data 5306 including thousands, tens of thousands or millions ofvehicle data instances.

Referring now to FIG. 53C, shown is a conceptual diagram of vehiclepaths 5314, 5315, and 5316 of vehicles corresponding to device ID111,device ID112, and device ID113, respectively. Vehicle paths 5314, 5315,and 5316 correspond to vehicle-specific datasets 5311, 5312 and 5313,respectively. Each vehicle path includes a plurality of vehicle-positionpoints 5308 representing a position of a vehicle at a different point intime. Vehicles travel in the direction of arrows 5323, 5326 and 5329, onvehicle paths 5314, 5315, and 5316, as shown.

According to an embodiment, a vehicle journey begins when a vehicletransitions from an undriveable state, i.e., ignition status is OFF, toa driving state, i.e., ignition state is ON, and has a speed greaterthan 0 km/hr, and a vehicle journey ends when the vehicle transitionsfrom a driving state to an undriveable state.

For instance, a beginning of a journey may be detected when a thirdvehicle data instance indicates the vehicle ignition state is OFFfollowed by a third vehicle data instance indicating the vehicleignition state of the vehicle is ON and that the vehicle speed isgreater than 0 km/h.

Alternatively, a vehicle journey begins in another way. For example, avehicle may not start increasing in speed immediately aftertransitioning from an ignition OFF to ON state. In some instances avehicle may idle for a period of time. A beginning of a journey may bedetected when a third vehicle data instance indicates the vehicleignition state is OFF followed by one or more third vehicle datainstances indicating the vehicle ignition state of the vehicle is ON andhas a vehicle speed 0 km/h, immediately followed by a third vehicle datainstance indicating the vehicle ignition state of the vehicle is ON andhas a vehicle speed is greater than 0 km/h. Alternatively, after apredefined period of idling, a journey may end and a new journey maybegin when a speed greater than 0 km/h is detected. Furtheralternatively, a beginning vehicle journey may be detected (i.e,defined) in another way.

Forming journey data is described below with reference to FIGS. 53D and53E. FIG. 53E is a conceptual diagram of vehicle journeys 5330, 5331,5332A and 5332B corresponding to journey data 5334, 5335, and, 5336A and5336B respectively. For clarity, some vehicle-position points 5308 onvehicle paths 5314, 5315, and 5316 are not shown in FIG. 53E.

In a first example, traffic analytics system 104 a processesvehicle-specific dataset 5311 for forming journey data 5334corresponding to a vehicle having device ID111. Traffic analytics system104 a may process third vehicle data instances in time consecutiveorder, beginning with a first third vehicle data instance ofvehicle-specific dataset 5311 corresponding to a first vehicle-positionpoint 5320 of vehicle path 5314. Alternatively, third vehicle datainstances are processed in a non time consecutive order.

Traffic analytics system 104 a processes vehicle-specific dataset 5313in a time sequential manner searching for a beginning sequence of thirdvehicle data instances indicating the ignition status of the vehicle isOFF, immediately followed by a second instance that indicates theignition status of the vehicle is ON and the vehicle has a speed greaterthan 0 km/h.

Additionally and/or optionally, a beginning sequence of third vehicledata instances indicates the ignition status of the vehicle is OFF,immediately followed by one or more a instances indicating the ignitionstatus of the vehicle is ON and has a speed that is 0 km/h, and then isimmediately followed by an instance indicating the ignition status ofthe vehicle is ON and the vehicle has a speed greater than 0 km/h.

Now referring to FIG. 53D, shown is exemplary journey data 5341including journey data 5334, 5335, 5336A and 5336B formed by trafficanalytics system 104 a. For clarity, vehicle-position point data 5308corresponding to the journey data are shown in FIG. 53D.

Third vehicle data instances are selected from vehicle-specific dataset5311 by traffic analytics system 104 a for forming journey data 5334.Traffic analytics system 104 a identifies a beginning sequence of thirdvehicle data instances indicating a vehicle transitioning from anundriveable state to a driving state corresponding to vehicle-positionpoints 5333A and 5333B, respectively, as shown in FIG. 54D. Thisbeginning sequence of third vehicle data instances indicates thebeginning of a vehicle journey and is selected from vehicle-specificdataset 5311 for forming journey data instances 5334A and 5334B ofjourney data 5334, as shown.

Traffic analytics system 104 a continues processing vehicle-specificdataset 5311 in time consecutive order searching for a next thirdvehicle data instance indicating an end of the journey. A nextconsecutive third vehicle data instance indicating an ignition statusOFF corresponds to vehicle-position point 5333E. This third vehicle datainstance is selected by traffic analytics system 104 a fromvehicle-specific dataset 5311 for forming journey data instance 5334E.

Traffic analytics system 104 a selects third vehicle data instances fromvehicle-specific dataset 5311 corresponding to vehicle-position points5333A and 5333E and all vehicle-position points 5308 therebetween, forforming journey data 5334 associated with journey 5330 shown in FIG.53E.

Traffic analytics system 104 a continues processing third vehicle datainstances from vehicle-specific dataset 5311 to identify a beginning ofanother journey, however, none is found.

In a second example, traffic analytics system 104 a processesvehicle-specific dataset 5312 for forming journey data 5335corresponding to a vehicle having device ID112 in a same manner asdescribed above. Third vehicle data instances are selected fromvehicle-specific dataset 5312 by traffic analytics system 104 a forforming journey data 5335. Traffic analytics system 104 a identifies abeginning sequence of third vehicle data instances indicating a vehicletransitioning from an undriveable state to a driving state correspondingto vehicle-position points 5338 and 5350, respectively, as shown in FIG.54D. This beginning sequence of third vehicle data instances indicatesthe beginning of a vehicle journey and is selected from vehicle-specificdataset 5312 for forming journey data instances 5335A and 5335B ofjourney data 5335, as shown.

Traffic analytics system 104 a continues processing vehicle-specificdataset 5312 in time consecutive order searching for a next thirdvehicle data instance indicating an end of the journey. A nextconsecutive third vehicle data instance indicating an ignition statusOFF corresponds to vehicle-position point 5337. This third vehicle datainstance is selected by traffic analytics system 104 a fromvehicle-specific dataset 5312 for forming journey data instance 5335E.

Traffic analytics system 104 a selects third vehicle data instances fromvehicle-specific dataset 5312 corresponding to vehicle-position points5338 and 5337 and all vehicle-position points 5308 therebetween, forforming journey data 5335 associated with journey 5331 shown in FIG.53E.

Traffic analytics system 104 a continues processing third vehicle datainstances from vehicle-specific dataset 5312 to identify a beginning ofanother journey, however, none is found.

In a third example, traffic analytics system 104 a processesvehicle-specific dataset 5313 for forming journey data 5336Acorresponding to a vehicle having device ID113 in a same manner asdescribed above. Third vehicle data instances are selected fromvehicle-specific dataset 5313 by traffic analytics system 104 a forforming journey data 5336A. Traffic analytics system 104 a identifies abeginning sequence of third vehicle data instances indicating a vehicletransitioning from an undriveable state to a driving state correspondingto vehicle-position points 5342A and 5342B, respectively, as shown inFIG. 54D. This beginning sequence of third vehicle data instancesindicates the beginning of a vehicle journey and is selected fromvehicle-specific dataset 5313 for forming journey data instances 5336Aand 5335B of journey data 5336, as shown.

Traffic analytics system 104 a continues processing vehicle-specificdataset 5313 in time consecutive order searching for a next thirdvehicle data instance indicating an end of the journey. A nextconsecutive third vehicle data instance indicating an ignition statusOFF corresponds to vehicle-position point 5342D. This third vehicle datainstance is selected by traffic analytics system 104 a fromvehicle-specific dataset 5313 for forming journey data instance 5376D.

Traffic analytics system 104 a selects third vehicle data instances fromvehicle-specific dataset 5313 corresponding to vehicle-position points5342A and 5342D and all vehicle-position points 5308 therebetween, forforming journey data 5336A associated with journey 5332A, shown in FIG.53E.

Traffic analytics system 104 a continues processing third vehicle datainstances from vehicle-specific dataset 5313 to identify a beginning ofanother journey.

Third vehicle data instances are selected from vehicle-specific dataset5313 by traffic analytics system 104 a for forming journey data 5336B.Traffic analytics system 104 a identifies a beginning sequence of thirdvehicle data instances indicating a vehicle transitioning from anundriveable state to a driving state corresponding to vehicle-positionpoints 5342D and 5342E, respectively, as shown in FIG. 54D. Thisbeginning sequence of third vehicle data instances indicates thebeginning of a vehicle journey. Traffic analytics system 104 a selects5376D corresponding to vehicle-position point 5342D and third vehicledata instance from vehicle-specific dataset 5313 for forming journeydata instances 5336F and 5335G of journey data 5336B, as shown.

Traffic analytics system 104 a continues processing vehicle-specificdataset 5313 in time consecutive order searching for a next thirdvehicle data instance indicating an end of the journey. A nextconsecutive third vehicle data instance indicating an ignition statusOFF corresponds to vehicle-position point 5342G. This third vehicle datainstance is selected by traffic analytics system 104 a fromvehicle-specific dataset 5313 for forming journey data instance 5336G.

Traffic analytics system 104 a selects third vehicle data instances fromvehicle-specific dataset 5313 corresponding to vehicle-position points5342D and 5342G and all vehicle-position points 5308 therebetween, forforming journey data 5336B associated with journey 5332B. Trafficanalytics system 104 a continues processing third vehicle data instancesfrom vehicle-specific dataset 5313 to identify a beginning of anotherjourney, however, none is found.

In this example, journey data 5341 includes device ID data 5341A,position data 5341B, (e.g., LAT/LONG coordinates), ignition state data5341C, date and time data 5341C (e.g., timestamp), and speed data 5341D,as shown. For ease of description not all third data is shown in journeydata 5403.

FIG. 53F is a conceptual diagram of exemplary vehicle journeys 5330,5331, 5332A and 5332B superposed on the first plurality of road networksubzones 4501. For ease of description, only 4 journeys are described inthis example. In practise, however, the number of journeys may includehundreds, thousands, or more.

Block 4614

At block 4614, process 4600 includes processing second road network dataand journey data for mapping each journey data instance thereof to aroad network subzone and forming new journey data to include anindication of that mapping.

Shown in FIG. 54A is a conceptual diagram of portion 5400 of the firstplurality of road network subzones 4501 defined by line A-A in FIG. 53F.FIG. 54A also shows vehicle-position points 5308 corresponding tovehicle journey 5331 superposed on portion 5400. FIG. 54B is an enlargedview of a portion 5401 of portion 5400 defined in FIG. 54A.

For example, traffic analytics system 104 a processes second roadnetwork data 5200 and journey data 5335 corresponding to journey 5331for mapping each journey data instance thereof to a road network subzone4502 of the first plurality of road network subzones 4501.

In a first example, traffic analytics system 104 a determines whetherjourney data instance 5335A associated with vehicle-position point 5338corresponds to a road network subzone 4502 of the first plurality ofroad network subzones 4501. In the present example, road networksubzones are defined according to the Geohash spatial hierarchy system.Traffic analytics system 104 a implements Geohash encode function 1108of FIG. 11B for mapping the location represented by vehicle-positionpoint 5338 to a Geohash string indicative of the Geohash within which itis located. In this instance, function 1108 returns a first Geohashstring, GeohashString-5402, corresponding to Geohash 5402, shown in FIG.54A.

Next, traffic analytics system 104 a processes second road network data5200 for determining whether Geohash 5402 is included in the firstplurality of road network subzones 4501. In particular, trafficanalytics system 104 a processes road network subzone location data 4512searching for a Geohash string that indicates Geohash 5402. In thisinstance, a match is not found indicating journey data instance 5335Adoes not correspond to any road network subzone 4502.

FIG. 54C and FIG. 54D are conceptual diagrams of exemplary modifiedjourney data 5403 formed by traffic analytics system 104 a. For clarity,vehicle-position point data 5308 corresponding to the journey data areshown in FIG. 53D. For clarity, vehicle-position point data 5380corresponding to the journey data are shown in FIG. 54C and FIG. 54D.For descriptive purposes only, a subset of data of each journey datainstance is shown.

Next traffic analytics system 104 a forms new journey data 5403including journey data 5341 and an indication of a corresponding roadnetwork subzone ID, road network subzone location, road network subzonelabel, and intersection mapping associated with each instance thereof.

In the present example, traffic analytics system 104 a forms journeydata instance 5410 of journey data 5403 including journey data fromjourney data 5341 corresponding to road network subzone 5402. Journeydata instance 5410 also includes road network subzone location data5404B indicating a Geohash string, GeohashString-5402. As journey datainstance 5334A does not correspond to any road network subzone 4502 roadnetwork subzone ID data 5404A, road network subzone label data 5404B andintersection mapping data 5404D of journey data instance 5335A have nullvalues.

In a second example, traffic analytics system 104 a determines, in asimilar manner as described above, whether a journey data instanceassociated with vehicle-position point 5352 maps to a road networksubzone 4502 of the first plurality of road network subzones 4501. Inthis example, the journey data instance of journey data 5341 associatedwith vehicle-position point 5352 maps to Geohash 5492, as shown in FIG.54B.

Next, traffic analytics system 104 a adds journey data instance 5411 tojourney data 5403. Journey data instance 5411 includes the journey datainstance from journey data 5341 corresponding to road network subzone5492, road network subzone ID data 5404A, indicating ID 5492, roadnetwork subzone location data 5406, indicating GeohashString-5492, roadnetwork subzone label data 5408 indicating a non-core subzone label, andintersection mapping data 5404D indicating intersection mapping 4802F,shown in FIG. 54C.

Traffic analytics system 104 a processes second road network data 5200and the remainder of journey data 5335, journey data 5334 and journeydata 5336, and forms journey data 5403 in a similar manner as describedabove. FIGS. 54C and 54D show examples of other journey data instancesof journey data 5335.

Block 4616

Next, at block 4616, process 4600 includes selecting at least a firstsequence of journey data instances from journey data for forming tripdata. According to some embodiments, the first sequence of journey datainstances includes at least one journey data instance corresponding to acore subzone and is mapped to a first intersection, and is immediatelyfollowed by a journey data instance mapped to a second intersection,wherein the second intersection and the first intersection are not thesame intersection.

According to another embodiment, at block 4616, process 4600 includesidentifying a first sequence of journey data instances and a secondsequence of journey data instances for forming trip data based on thefirst sequence of journey data and the second sequence of journey data.A second sequence of journey data instances includes at least a journeydata instance corresponding to a non-core subzone mapped to the firstintersection immediately preceding the first sequence of journey datainstances.

Forming trip data also includes mapping each trip data instance to thefirst intersection and storing an indication of the mapping therein.

In a first example, traffic analytics system 104 a processes journeydata 5403 for identifying a first sequence of journey data instances.Referring again to FIG. 54C, journey data instances 5413, 5415, 5417,5419, 5421, 5429 and 5439 correspond to core subzones and are mapped toa first intersection, intersection 4802F. Journey data instances 5413,5415, 5417, 5419, 5421, 5429 and 5439 also immediately precede journeydata instance 5339 which corresponds to a non-core subzone mapped to asecond intersection, intersection 4802G. As such, traffic analyticssystem 104 a identifies a first sequence of journey data instances 5490including journey data instances 5413, 5415, 5417, 5419, 5421, 5429 and5439, each thereof corresponding to a core subzone mapped to a firstintersection, (i.e., intersection 4802F) and is immediately followed bya journey data instance (i.e., instance 5339) mapped to a secondintersection, intersection 4802G.

In this example, traffic analytics system 104 a also identifies a secondsequence of journey data instances 5491 shown in FIG. 54C. Journey datainstance 5411 corresponds to a non-core subzone mapped to the firstintersection, intersection 4802F, and immediately precedes the firstsequence of journey data instances 5490. As such, traffic analyticssystem 104 a identifies a second sequence of data journey instances5491. In this example, the second sequence of journey data instance(s)5491 includes a sequence of one journey data instance. In otherinstances, the second sequence of journey data instance(s) may include aplurality of journey data instances.

Next, traffic analytics system 104 a forms trip data dependent on thefirst sequence of journey data instances 5490 and the second sequence ofjourney data instance(s) 5491. Exemplary trip data 5500 is shown in FIG.55A, including position data 5502, date and time data 5504, speed data5506, road network subzone ID data 5508, road network subzone locationdata 5510, and road network subzone label data 5512. Trip data 5500further includes intersection mapping data 5514 corresponding to thefirst intersection, intersection 4802F. Alternatively, trip data isformed based on the first sequence of journey data instances. Forclarity, vehicle-position point data 5516 corresponding to the trip datais shown in FIG. 55A.

A conceptual diagram of trip 5551 corresponding to trip data 5500 isshown in FIG. 55B. As described herein above, arrow 5326 indicates thedirection of travel of the corresponding vehicle. In this example, trip5551 begins in road section 4806-11, as indicated by vehicle-positionpoint 5352, traverses intersection 4802F, as indicated byvehicle-position points 5353, 5354, 5355, 5356A, 5356B, 5356C, and endsjust before entering road section 4806-12, as indicated byvehicle-position point 5357.

In a second example, traffic analytics system 104 a processes journeydata 5403 for identifying another first sequence of journey datainstances. Referring now to FIG. 54D, each of journey data instances5432, 5434, 5436, 5438, and 5440 corresponds to a core subzone mapped toa first intersection 4802G and immediately precedes journey datainstance 5442 which corresponds to a non-core subzone mapped to a secondintersection, intersection 4802F. As such, traffic analytics system 104a identifies a first sequence of journey data instances 5493 includingjourney data instances 5432, 5434, 5436, 5438, and 5440.

Traffic analytics system 104 a also identifies a second sequence ofjourney data instances 5494 shown in FIG. 54D. Consecutive journey datainstances 5339, 5428, and 5431, correspond to a non-core subzones mappedto the first intersection, intersection 4802G. Journey data instances5439, 5428, and 5431, immediately precede the first sequence of journeydata instances 5493, as shown. As such, traffic analytics system 104 aidentifies a second sequence of data journey instances 5494.

Next, traffic analytics system 104 a forms trip data dependent on thefirst sequence of journey data instances 5493 and the second sequence ofjourney data instances 5494. Exemplary trip data 5570 is shown in FIG.55C, including position data 5502, date and time data 5504, speed data5506, road network subzoneID data 5508, road network subzone locationdata 5510, and road network subzone label data 5514. Trip data 5570further includes intersection mapping data 5514 corresponding to thefirst intersection, intersection 4802G. As such, traffic analyticssystem 104 a remaps journey data instances 5439, 5428 and 5431 tointersection 4802G only, as shown in FIG. 55C. For clarity,vehicle-position point data 5516 corresponding to the trip data is shownin FIG. 55C.

A conceptual diagram of a trip 5552 corresponding to trip data 5570 isshown in FIG. 55B. As described herein above, arrow 5326 indicates thedirection of travel of the corresponding vehicle. In this example, trip5552 begins in road section 4806-12, as indicated by vehicle-positionpoint 5358, traverses road section 4806-12, as indicated byvehicle-position points 5359 and 5360, then traverses intersection 4802,as indicated by vehicle-position points 5361, 5362, 5363, 5364, and endsjust before entering intersection 4802H, as indicated byvehicle-position point 5365.

Traffic analytics system 102 a continues to process journey data 5570for identifying other first sequences of journey data instances, andoptionally, other second sequences of journey data instances for formingtrip data as described hereinabove, and stores trip data, for example,in datastore 304.

FIG. 55D is a conceptual diagram of exemplary vehicle trips 5551, 5552,5554, and 5556, indicative of trip data created by traffic analyticssystem 102 based on journey data 5403. Trip 5554 begins when the vehicleenters intersection core 4802H, as indicated by vehicle-position point5366 and ends just before entering road section 4806-13, as indicated byvehicle-position point 5368.

Still referring to FIG. 55D, journey 5301 exits road network 4500 asindicated by vehicle-position point 5370 and returns thereto asindicated by vehicle-position point 5372. Trip 5556 begins where journey5330 returns to road network 4500 at road section 4806-13 as indicatedby vehicle-position point 5372, and ends just before entering roadsection 4806-15, as indicated by vehicle-position point 5375. Trafficanalytics system 104 a associates trips 5554, and 5556 withintersections H and I, respectively.

Traffic analytics system 102 a processes remaining journey data, i.e.,modified journey data 5334 and 5536, for identifying other firstsequences of journey data instances, and optionally, other secondsequences of journey data instances for forming trip data as describedhereinabove, and stores trip data, for example, in datastore 304. FIG.55E shows conceptual diagrams of exemplary vehicle trips 5560, 5561,5562, 5563, 5564 and 5565, indicative of trip data created by trafficanalytics system 104 a after processing remaining journey data, i.e.,modified journey data 5334 and 5536.

For ease of description, only ten trips are described in this example.In practise, however, the number of trips may include hundreds,thousands, or more.

In some embodiments, a vehicle may depart from the road network during atrip and should the vehicle remain outside the road network for a periodexceeding a predefined length time corresponding journey data instancesare ignored. A specific and non-limiting example of a predefined lengthtime includes 10 s. In this example, should a vehicle exit the roadnetwork for more than 10 s, journey data instances associated with thevehicle while outside the road network will be ignored when journey datais processed for creating trip data therefrom.

Referring now to FIG. 56A, shown is a conceptual diagram of exemplarytrip data 5600 created by traffic analytics system 102 a and storedthereby, for example, in a datastore 304. For clarity, trip data 5600 isshown organized according to the intersection to which the trip data ismapped. For instance, trip data 5600 includes trip data 5600A, 5600B,5600C, 5600D, 5600E, 5600F, 5600G, 5600H, and 5600I, including trip datamapped to intersections A, B, C, D, E, F, H and I, respectively. Forexample, trip data 5600F includes trip data 5500 corresponding to trip5551 and trip data 5600G includes trip data 5570 corresponding to trip5552. One of ordinary skill appreciates that trip data may be organizedin numerous manners.

Block 4618—Trip Metadata

Next, at block 4618, process 4600 includes processing trip data forcreating trip metadata. Specific and non-limiting examples of trip dataare provided in Table 13 below.

TABLE 13 Trip Metadata Trip Metadata Description HardwareId indicates adeviceID Vin indicates a Vehicle Identification Number Make indicates amake of the vehicle Model indicates a model of the vehicle VehicleYearindicates a year of the vehicle WeightClass indicates a maximum loadedrate of vehicle, for example, Class A (3000 lbs and under) Class B(3001-4000 lbs) Class C (4001-5000 lbs) Class D (5001-6000 lbs) ClassE(6001-7000 lbs) Class F (7001-8000 lbs) Class G(8001-9000 lbs) Class H(9001-10,000 lbs) Class 3 (10,001-14,000 lbs) Class 4 (14,001-16,000lbs) Class 5 (16,001-19,500 lbs) Class 6 (19,501-26,000 lbs) Class 7(26,001-33,000 lbs) Class 8 (33,001 and over) VehicleType indicates atype of vehicle, for example, passenger car minivan truck bus limo otherVocation indicates a vocation of the vehicle routed delivery,hub-and-spoke, patroller, etc. TripID indicates a unique trip IDassigned to each trip IntersectionId indicates a unique intersection IDTimezoneName indicates a timezone in which intersection resides, e.g.,AST, EST, CST, PST, etc. EventStartTimeUTC indicates time a trip beginsin coordinated universal time (UTC) EventEndTimeUTC indicates time atrip ends in coordinated universal time (UTC) EventStartTimeLocalindicates time a trip begins in time zone in which intersection residesEventEndTimeLocal indicates time a trip ends in time zone in whichintersection resides StartingLocation indicates a location trip starts,examples include, LAT/LONG coordinates, GPS coordinates EntryCardinalindicates a direction vehicle is heading when it enters an intersection,e.g., N, NE, E, SE, SW, W, NW ExitCardinal indicates a direction vehicleis heading when it exits an intersection, e.g., N, NE, E, SE, SW, W, NWTravelTime indicates a total travel time of the trip(EventEndTimeUTC-EventStartTimeUTC) TravelDistance indicates a totaltravel distance travelled by the vehicle during the trip Travel Speedindicates a travel speed of the trip (TravelDistance/TravelTime)RunningTime indicates a total amount of time vehicle is moving duringthe trip RunningSpeed indicates TravelDistance/RunningTime StopTimeTotalindicates a total amount of time vehicle was stopped i.e., speed was 0km/h during trip, NumberOfStops indicates a total number of stops duringthe trip TimeFromFirstStop indicates a time elapsed from the first stopto the end of the trip DistanceFromFirstStop indicates a distance from acenter of the intersection to a first stop location

According to an embodiment a portion of trip metadata is available inpre-compiled VIN look up tables. For example, traffic analytics system104 a accesses pre-compiled VIN look up table(s) including manufacturerdata, and vocation data associated with a vehicle's VIN. Trafficanalytics system 104 a system may store VIN look up tables, for examplein datastore 304. Alternatively, and/or additionally, a VIN look uptable(s) is available on a remote server(s) accessible by trafficanalytics system 104 a via communication network 110. Alternatively,and/or additionally trip metadata is accessible by traffic analyticssystem 104 in another manner.

For example, traffic analytics system 104 a accesses a VIN look up tablestored in datastore 302 for obtaining manufacturer data, and vocationdata and of a vehicle corresponding to VIN 19ABA65576A061968, asindicated by a trip data instance. Exemplary manufacturer data obtainedby traffic analytics system 104 a includes vehicle type data, make data,model data, year data, weight class data. Manufacturer data may includeother vehicle specification and/or related data.

Vocation data indicates a vocation of a vehicle. Vocation of a vehiclecan be automatically determined, for example as described in U.S. Pat.No. 10,928,277 issued to Geotab Inc., the contents of which areincorporated herein in their entirety.

In a first example, traffic analytics system 104 a processes trip data5600F for creating trip metadata relating to trips mapped tointersection 4802F. For instance, traffic analytics system 104 aprocesses trip data 5500 associated with trip 5551 for forming tripmetadata corresponding thereto.

Shown in FIG. 56C is exemplary trip metadata 5610F formed by trafficanalytics system 104 a, including tripID data 5610E-1 indicating a tripID of 5551, HardwareId data 5610E-2 indicating device ID112, Vin data5610E-4 indicating the corresponding vehicle VIN is 12345678910123456,make data 5610E-5 indicating the vehicle make is Mercedes-Benz, modeldata 5610E-6 indicating the vehicle model is Powertrain, VehicleYeardata 5610E-7 indicating the vehicle year is 2020, WeightClass data5610E-8 indicating the corresponding vehicle is categorized as a class 4vehicle, VehicleType data 5610E-9 indicating the vehicle is a truck,Vocation data 5610E-10 indicating the vehicle's vocation is ‘delivery’,IntersectionId data 5610E-13 indicating the trip is mapped tointersection 4802F. In this example, traffic analytics system 104 aaccesses a pre-compiled VIN look up table(s) stored in datastore 304 forobtaining make data, model data, year data, WeightClass data,VehicleType data and Vocation data.

In some instances, traffic analytics system 104 a receives a timezoneshapefile, from, for example, a publicly accessible server of agovernment geoscientific agency. For instance, the shapefile indicatesboundaries of world timezones with attributes giving the identity ofeach timezone and UTC offset. Traffic analytics system 104 a reversegeocodes all geohashes in each timezone and determines the name of thetime zone the intersection is located and UTC offset by mapping at leasta core subzone (i.e., geohash) from each intersection core to a timezonesubzone. In the present example, traffic analytics system has mappedintersection 4802F to the Eastern Standard Timezone.

Trip metadata 5610F for trip 5551 also includes EventStartTimeUTC data5610E-26 indicating the trip began 23/01/18-14:35:44 in UTC,EventEndTimeUTC data 5610E-27 indicating the trip ended23/01/18-14:37:08 UTC. EventStartTimeLocal data 5610E-28 indicating thetrip started 23/01/18-10:35:44 EST, EventEndTimeLocal data 5610E-29indicating trip 5500 ended 23/01/18-10:37:08 EST. To generateEventStartTimeLocal data 5610E-28 and EventEndTimeLocal data 5610E-29,traffic analytics converts EventStartTimeLocal data 5610E-28 andEventEndTimeLocal data 5610E-29 from UTC to EST.

Trip metadata 5610F also includes StartingLocation data 5610E-30indicating trip 5500 started at the location 43o12′37.05″ N79o46′58.879″ W.

Trip metadata 5610F also includes TravelTime data 5610E-37 indicatingthe total travel time of the vehicle during the trip was 34 s,TravelDistance data 5610E-38 indicating the vehicle travelled 89 mduring the trip, TravelSpeed data 5610E-38 indicating the average speedof the vehicle during the trip was 2.7 km/h, RunningTime data 5610E-40indicating the total amount of time the vehicle was moving during thetrip was 34 s, RunningSpeed data 5610E-41 indicatingDistanceTravelled/RunningTime was 2.7 km/h, StopTimeTotal data 5610E-44indicating the total amount of time the vehicle was stopped during thetrip was 0 s, NumberOfStops data 5610E-45 indicating the total number ofstops during the trip was 0, TimeFromFirstStop data 5610E-56 indicatingthe elapsed time between the first stop to the end of the trip is nullsince the vehicle never stopped, and DistanceFromFirstStop data 5610E-47indicating the distance from the centre of the intersection to the firststop location was null since the vehicle never stopped. A center of theintersection may be determined by using the corresponding intersectionreference point 4718G.

Trip metadata 5610F also includes EntryCardinal data 5610E-31 indicatingthe direction the vehicle was heading when it entered the intersectionwas N, and ExitCardinal data 5610E-32 indicating the direction thevehicle was heading when it exited the intersection was W.

A description of A method for determining EntryCardinal and ExitCardinalis described below with reference to FIG. 56B.

According to an embodiment, there is a process for determining an entrycardinal of a vehicle entering an intersection including processingposition data of the first instance of trip data located in the core andprocessing position data of an immediately preceding instance of journeydata. In particular, processing includes determining the bearing, i.e.,heading angle, between position data corresponding to the first instanceof trip data located in the core and the immediately preceding instanceof journey data.

For example, traffic analytics system 104 a calculates the bearing, β,with formula:

β=atan 2(X,Y),

X is defined as, X=cos θb*sin ΔL

Y is defined as, Y=cos θa*sin θb−sin θa*cos θb*cos ΔL

‘L’ is defined as longitude in decimal format form,

‘θ’ is defined as latitude in decimal format form.

Bearing, β is measured from the North direction. In this example, 0°bearing means N, 45° bearing means NE, 90° bearing is E, 135° bearing isSE, 180° bearing is S, 225° bearing is SW, 270° bearing is W, and 315°bearing is NW. Referring now to FIG. 56B, is a table 5601 showingassigned cardinal direction based on the bearing. For example, a bearingof 346.5° is assigned cardinal direction N.

In a first example, traffic analytics system 104 a calculates the entrybearing trip 5552. Referring again to FIG. 55B, the first instance oftrip data located in the core of trip 5552 is associated withvehicle-position point 5361 and the immediately preceding instance ofjourney data is associated with vehicle-position point 5360, as shown.In this example, the immediately preceding instance of journey dataassociated with vehicle-position point 5360 corresponds to trip datainstance 5431.

Position data 5502 corresponding to trip data instance 5342 is 43o12′39″N, 79o 47 31″W and position data 5502 corresponding to trip datainstance 5341 is 43o 12′ 38″N, 79o 47′ 20″W. Traffic analytics system104 a determines the heading angle, between the points indicated by 43o12′39″N, 79o 47 31″W and 43o 12′ 38″N, 79o 47′20″W.

Firstly, traffic analytics system 104 a converts point a, 43o 12′ 38″N,79o 47′ 20″W and point b 43o 12′ 39″N, 79o 47 31″W into decimal degreeformat. For instance, point a, 43o 12′ 38″N, 79o 47′ 20″W converted todecimal format is 43.210556, 79.791667. Point b, 43o 12′ 39″N, 79o 4731″W converted to decimal format is 43.210833, 79.784167.

Next, traffic analytics system 104 a determines X wherein

X=cos θb*sin ΔL

ΔL=79.791667−79.784167=0.0075

X=cos(43 0.210833)*sin(0.0075)

Y=cos θa*sin θb−sin θa*cos θb*cos ΔL

β=atan 2((cos(43 0.210833)*sin(0.0075)),(cos(43 0.210556)*sin(430.210833)−sin(43.210556)*cos(43.210833)*cos(0.0075)))

β=272.9°

Referring again to table 5601 in FIG. 55B, a bearing of 272.9ocorresponds to a cardinal direction of table 5601, traffic analyticssystem assigns a cardinal entry of W. As such, traffic analytics system104 a creates EntryCardinal data for trip metadata to indicate anEntryCardinal of West. For instance, the vehicle corresponding to tripID 5552 enters the intersection heading west, as shown in FIG. 55B.

According to an embodiment, there is a process for determining an exitcardinal of a vehicle exiting an intersection including processingposition data of the last instance of trip data located in the core andprocessing position data of an immediately following instance of journeydata. In particular, processing includes determining the bearing, i.e.,heading angle, between position data corresponding to the last instanceof trip data located in the core and the immediately following instanceof journey data.

For example, traffic analytics system 104 a calculates the exit bearingmetadata for trip 5552. Referring again to FIG. 55C, the last instanceof trip data located in the core of trip 5552 is associated withvehicle-position point 5365 and the immediately following instance ofjourney data is associated with vehicle-position point 5366, as shown.

Traffic analytics system 104 a processes the position data of the tripdata instance associated with trip data 5440, trip data instance 5440,and position data of journey data instance corresponding tovehicle-position point 5366 in a same manner as described above.

Traffic analytics system 104 a calculates a bearing, β=271o. Referringagain to table 5601 in FIG. 55B, a bearing of 271o corresponds to acardinal direction W. As such, traffic analytics system 104 a createsExitCardinal data for trip metadata to indicate an ExitCardinal of West.For instance, the vehicle corresponding to trip ID 5552 exits theintersection heading west.

Traffic analytics system 104 a processes remaining trip data for formingtrip metadata. Shown in FIG. 57A is exemplary trip metadata 5700generated by traffic analytics system 104 and stored, for example, indatastore 304. Trip metadata 5700 is shown organized according to anintersection to which it is mapped, however, trip metadata may beorganized in any manner.

Additionally/optionally, other trip metadata is formed, such as,

MaxAcceleration indicating a maximum acceleration of vehicle during tripMinAcceleration indicating a minimum acceleration of vehicle during tripMaxSpeed indicating a maximum speed of vehicle during trip MinSpeedindicating a minimum speed of vehicle during trip SignalUsed indicatingwhether a turn signal was used (true) or not used (false) during thetrip TurnSignals indicating the direction of the turn signal (left orright) and a timestamp StreetNameEntry indicating a street name fromwhich the vehicle entered the intersection StreetNameExit indicating astreet name onto which the vehicle exited the intersection

Additionally, and/or optionally, traffic analytics system 104 a mapscore subzones to a street name of a street that is determined to be thenearest street thereto. For example, traffic analytics system 104 aforms a shape file based on the area occupied by core subzones of anintersection core. Using data from the shape file obtains road data froman OSM server relating to roads overlapping the shape in the shape file.Next, traffic analytics system 104 a maps each core subzone of anintersection core to the nearest road within boundaries of the shape ofthe shapefile and stores an indication of the second road network data.During trip metadata generation, in block 4618, traffic analytics system104 a generates StreetNameEntry data indicating the name of the roadassociated with the first core subzone of the trip. Next, trafficanalytics system 104 a generates StreetNameExit data indicating the nameof the road associated with the last core subzone of the trip.

Block 4620

Finally, at block 4620, process 4600 includes processing trip metadatafor forming intersection metrics data. In some embodiments, processingtrip metadata includes processing trip metadata and filter data. In someembodiments, processing trip metadata includes processing trip metadata,trip data and filter data.

Specific and non-limiting examples of intersection metrics are providedin Table 14 below.

TABLE 14 Intersection Metrics Percentage Stopping data Percentage ofvehicles which came to at least one complete stop before passing throughthe intersection AvgTravel Speed data Average travel speed through theintersection (total distance over total time) AvgRunningSpeed dataAverage running speed through the intersection (total distance overmoving time) AvgTotalTimeStopped data Average total time spent at zerospeed AvgTotalTimeStoppedNoZero data Average total time spent at zerospeed (excluding vehicles which did not stop) AvgTravelTime data Averagetotal travel time through the intersection AvgTimeFromFirstStop dataAverage time from the first stop to travelling through the intersectionAvgNumberOfStops data Average number of stops made before passingthrough the intersection AvgNumberOfStopsNoZero data Average number ofstops made before passing through the intersection (excluding vehicleswhich did not stop) AvgDistanceFromIntersection- Average distance fromthe first stop to travelling ToFirstStop data through the intersectionPercentOfVolumeByVehicleClass data Percent of the total volume throughthe intersection, broken down by class of vehicle NumberOfTrips dataTotal number of trips through the intersection

Filter data indicates which trip metadata is to be processed forgenerating intersection metrics. Exemplary filter data 5702 is shown inFIG. 57B. In this example, filter data is provided by a user via a userinterface. Alternatively, filter data is provided in file format andreceived, for example, via network interface 306.

Filter data 5702 includes intersectionID data 5702A, EntryCardinal data5702B, ExitCardinal data 5702C, EventStartTimeLocal data 5702D andEventStopTimeLocal data 5702E. Intersection ID data 5702A indicatesintersection metrics for intersection 4802G are to be calculated.EntryCardinal data 5702B and ExitCardinal data 5702C indicate only tripmetadata associated with a trip having a North entry bearing and an Eastexit bearing should be processed.

Finally, EventStartTimeLocal data 5702D and EventStopTimeLocal data5702E indicate that only trip metadata corresponding to trips having astart time between Jan. 1, 2020 at 1 AM and Jan. 1, 2021 at 1 AM are tobe processed for generating metrics.

Alternatively, filter data includes cross-streets data indicating thestreets forming the intersection for indicating the intersection forwhich metrics are to be calculated. For instance, cross street dataincludes StreetNameEntry data and StreetNameExit data.

Alternatively, filter data includes one or more trip metadata. Somespecific and non-limiting examples include vocation, direction oftravel, make, model, vehicle year, weightclass and vehicle type.

Alternatively, filter data includes one or more days of the week.

Traffic analytics system 104 a selects a subset of trip metadata basedon filter data 5702 for forming filtered trip metadata 5703 shown inFIG. 57C. Next, traffic analytics system 104 a processes filtered tripmetadata 5703 for calculating intersection metrics data for intersection4802G.

Exemplary intersection metrics data 5704 is shown in FIG. 57D including,

NumberOfTrips data 5704A indicating the total number of filtered tripmetadata instances included in filtered trip metadata is 10,000 i.e.,the total number of trips represented by filtered trip metadata is10,000 trips,

AvgTravelSpeed data 5704B indicating the average travel speed throughthe intersection is 60 km/h. For example, traffic analytics system 104 acalculates AvgTravel Speed data by dividing the sum of TravelDistanceindicated by each filtered trip data instance by the sum of TravelTimeindicated by each filtered trip data instance,

AvgRunningSpeed data 5704C indicating the average running speed throughthe intersection is 100 km/h. For example, traffic analytics system 104a calculates the AvgRunningSpeed by dividing the sum of TravelDistanceindicated by each filtered trip data instance by the sum of TravelTimefrom each filtered trip data instance,

AvgTravelTime data 5704D indicating the average trip time is 156 s. Forexample, traffic analytics system 104 a calculates AvgTravelTime bydividing the sum of TravelTime indicated by each filtered trip datainstance by the total number of trips, i.e., 10,000,

PercentOfVolumeByVehicleClass data 5704E indicating the percentagevolume of trips associated with each each vehicle class is: ClassA—5%,Class B—5%, Class C—7%, Class D—10%, Class E—3%, Class F—10%, ClassG—5%, Class H—5%, Class 3—30%, Class 4—2%, Class 5—4%, Class 6—4%, Class7—5%, Class 8—5%. For example, traffic analytics system 104 a calculatesPercentOfVolumeByVehicleClass by, for each vehicle class, dividing thesum of trips associated with each vehicle class by the total number oftrips, i.e., 10,000,

PercentageStopping data 5704F indicating the percentage of trips whereinthe corresponding vehicle stopped at least once during the trip is 70%.For example, traffic analytics system 104 a calculatesPercentageStopping by dividing the sum of each filtered trip metadatainstance indicating NumberofStops data is greater than zero the totalnumber of trips, i.e., 10,000,

AvgNumberOfStops data 5704G indicating the average number of timesvehicles stopped during a trip is 2. For example, traffic analyticssystem 104 a calculates AvgNumberOfStops by dividing the sum ofNumberofStops Data indicated by each filtered trip metadata instance bythe total number of trips, i.e., 10,000.

AvgDistanceFromIntersectionToFirstStop data 5704H indicating the averagedistance from the first location a vehicle stops from the intersectioncore to the time the vehicle enters the intersection core during a tripis 6 m. For example, traffic analytics system 104 a calculatesAvgDistanceFromIntersectionToFirstStop by dividing the sum ofDistanceFromFirstSTop indicated by each filtered trip metadata instanceby the total number of trips, i.e., 10,000. Alternatively, anothercenter location is determined to be a centre location of theintersection core,

AvgTotalTimeStopped data 5704I indicating average total time a vehiclewas stopped, i.e., speed was 0 km/h, is 50 s. For example, trafficanalytics system 104 a calculates AvgTotalTimeStopped by dividing thesum of StopTimeTotal indicated by each filtered trip metadata instanceby the total number of trips, i.e., 10,000,

AvgTimeFromFirstStop data 5704J indicating average time between avehicle stoopin a first time to the time the vehicle exits theintersection core is 75 s. For example, traffic analytics system 104 acalculates AvgTimeFromFirstStop by dividing the sum of TimeFromFirstStopindicated by each filtered trip metadata instance by the total number oftrips, i.e., 10,000,

AvgTotalTimeStoppedNoZero data 5704K indicating average total time avehicle was stopped, i.e., speed was 0 km/h, (excluding vehicles whichdid not stop) during a trip is 40 s. For example, traffic analyticssystem 104 a determines the total number of trips having NumberofStopsgreater than zero, for instance, 7,000 out of 10,000 filtered metadatainstances indicated NumberofStops greater than zero. Next, trafficanalytics system 104 a calculates AvgTotalTimeStoppedNoZero by dividingthe sum of TimeFromFirstStop indicated by each filtered trip metadatainstance by the total number of trips having NumberofStops greater thanzero, i.e., 7,000,

AvgNumberOfStopsNoZero data 5704L indicating the average number of stopsmade during a trip before passing through the intersection (excludingvehicles which did not stop) is 3. For example, traffic analytics system104 a determines the total number of trips having NumberofStops greaterthan zero, for instance, 7,000 out of 10,000 filtered metadata instancesindicated NumberofStops greater than zero. Next traffic analytics system104 a calculates AvgNumberOfStopsNoZero by dividing the sum ofNumberofStops Data indicated by each filtered trip metadata instance bythe total number of trips having NumberofStops greater than zero, i.e.,7,000.

Additionally and/or optionally, other intersection metrics arecalculated based on trip metadata and corresponding intersection metricsdata is formed.

Next, traffic analytics system 104 a provides intersection metrics data,for example, to a user via a user interface. Alternatively, intersectionmetrics data is stored in a data file, for instance, in datastore 304,for future use.

Corridor Metrics Process 5800

According to an embodiment there is a process for determining corridormetrics for a road network corridor. A network corridor includes aplurality of contiguously connected intersections. FIG. 58A shows a flowdiagram of a process 5800 for determining corridor metrics for a roadnetwork corridor.

Process 5800 includes blocks 4602 to block 4620 of process 4600.

Once intersection metrics data has been formed, process 5800 proceeds atblock 5802. At block 5802, intersection metrics data and corridor dataare processed for forming corridor metrics data. Corridor data indicatesthe intersections that form a corridor for which metrics are to becalculated.

For example, corridor data is provided to traffic analytics system 104 aby a user, for example, via a user interface. Alternatively, trafficanalytics system 104 a receives corridor data in a data file, forexample, via network interface 306. In this example, a user specifies incorridor data that the corridor includes intersections 4802A, 4802B,4802C, 4802D, 4802G and 4802F, i.e., intersections A, B, C, D, G, and Fof road network 4501. Shown in FIG. 58B is a conceptual diagram of anexemplary corridor 5804 including intersections 4802A, 4802B, 4802C,4802D, 4802G and 4802F, i.e., intersections A, B, C, D, G, and F. Inthis example, a corridor includes 6 intersections, however, a corridormay include 2 intersections or more.

Next, traffic analytics system 104 a processes trip metadata 5700 forforming intersection metrics data 5806 including, 5806A, 5806B, 5806C,5806D, 5806G, and 5806F, for each intersection A, B, C, D, G, and F,respectively, of corridor 5804, as shown in FIG. 58C. In this example,Exemplary intersection metrics 5806A for intersection A are shown inFIG. 58D.

Next, traffic analytics system 104 a processes intersection metrics data5806A, 5806B, 5806C, 5806D, 5806G, and 5806F, for generating corridormetrics for corridor 5804.

Specific and non-limiting examples of corridor metrics are provided inTable 15 below.

TABLE 15 Corridor Metrics Corridor Percentage of vehicles which came toat least one Percentage Stopping data complete stop before passingthrough the corridor Corridor Average travel speed through thecorridor(total AvgTravel Speed data distance over total time) CorridorAverage running speed through the corridor(total AvgRunningSpeed datadistance over moving time) Corridor Average total time spent at zerospeed AvgTotalTimeStopped data Corridor Average total time spent at zerospeed (excluding AvgTotalTimeStoppedNoZero data vehicles which did notstop) Corridor Average total travel time through the corridorAvgTravelTime data Corridor Average time from the first stop totravelling AvgTimeFromFirstStop data through the corridor CorridorAverage number of stops made before passing AvgNumberOfStopsNoZero datathrough the corridor (excluding vehicles which did not stop) CorridorAverage distance from the first stop to travellingAvgDistanceFromIntersection- through the corridor ToFirstStop dataCorridor Percent of the total volume through the corridor,PercentOfVolumeByVehicleClass data broken down by class of vehicleCorridor Total number of trips through the corridor NumberOfTrips data

For example, traffic analytics system 104 a divides the sum of anintersection metric of all intersections A, B, C, D, G, and F, by thenumber of intersections, i.e., 6, for determining a correspondingcorridor metric. For instance, traffic analytics system 104 a sumsPercentageStopping metrics from intersections A, B, C, D, G, and F anddivides that sum by 6 to calculate a Corridor PercentageStopping metricand forms corresponding corridor PercentageStopping metric data.

Traffic analytics system 104 a processes the remainder of intersectionmetrics data 5806 for forming corridor metrics for corridor ABCDGF.Exemplary corridor metrics 5808 for corridor ABCDGF is shown in FIG.58D.

Additionally, and/or optionally, traffic metrics between one or morepairs of contiguous intersections of a corridor is determined. Acontiguous pair of intersections includes two intersections connected ina road network via a road section. In other words, there is no otherintersection between the path of a vehicle from a first intersection ofthe pair to the second intersection of the pair. Examples of contiguouspairs include AB, BC, CD, DG and GF.

For example, traffic analytics system 104 a divides the sum of anintersection metric of intersections A and B, by the number ofintersections, i.e., 2, for determining a corresponding traffic metrictherebetween. For instance, traffic analytics system 104 a sumsPercentageStopping metrics from intersections A and B, and divides thatsum by 2 to calculate AB PercentageStopping metric and formscorresponding AB PercentageStopping metric data.

Traffic analytics system 104 a processes intersection metrics data 5806Aand 5806B for forming AB traffic metrics data 5810AB, intersectionmetrics data 5806B and 5806C for forming BC traffic metrics data 5810BC,intersection metrics data 5806C and 5806D for forming CD traffic metricsdata 5810CD, intersection metrics data 5806D and 5806G for forming DGtraffic metrics data 5810DG, and intersection metrics data 5806G and5806F for forming GF traffic metrics data 5810GF, shown in FIG. 58E.

Additionally, and/or optionally, cumulative corridor metrics includingcorridor metrics for sequences of contiguous intersections of acorridor. Examples of sequences of contiguous intersections of corridor5804 include intersections ABC, ABCD, and ABCDG.

For example, traffic analytics system 104 a divides the sum of anintersection metric of intersections A, B and C, by the number ofintersections, i.e., 3, for determining a cumulative metric. Forinstance, traffic analytics system 104 a sums PercentageStopping metricsfrom intersections A, B and C, and divides that sum by 3 to calculateABC cumulative metric PercentageStopping and forms corresponding ABCPercentageStopping metric data. Exemplary ABC cumulative metric data5814ABC for sequence ABC is shown in FIG. 58F.

Traffic analytics system 104 a processes intersection metrics data5806A, 5806B and 5806C for forming ABC cumulative metric data 5814ABC,intersection metrics data 5806A, 5806B, 5806C and 5806D for forming ABCDcumulative metric data 5814ABCD, intersection metrics data 5806A, 5806B,5806C, 5806D and 5606G for forming ABCDG cumulative metric data5814ABCDG, shown in FIG. 58G.

For ease of description in the discussion above, monitoring device isdescribed as a physical device, however, it may be a component of alarger system, such as a vehicle communication system. Alternatively,the monitoring device is not a physical device and is integrated into acomponent of a larger system configured to perform operations/processesdescribed herein. Further alternatively, the monitoring device mayoperate as a virtual device of a system.

One of ordinary skill in the art will appreciate that there are varioustechniques for defining data indicative of geographical coordinates ofboundaries of a road network.

Included in the discussion above are a series of flow charts showing thesteps and acts of various processes. The processing and decision blocksof the flow charts above represent steps and acts that may be includedin algorithms that carry out these various processes. Algorithms derivedfrom these processes may be implemented as software integrated with anddirecting the operation of one or more processors, may be implemented asfunctionally-equivalent circuits such as a Digital Signal Processing(DSP) circuit, a Field Programmable Gate Array (FPGA), anApplication-Specific Integrated Circuit (ASIC), or may be implemented inany other suitable manner. It should be appreciated that the flow chartsincluded herein do not depict the syntax or operation of any circuit orof any programming language or type of programming language. Rather, theflow charts illustrate the functional information one skilled in the artmay use to fabricate circuits or to implement computer softwarealgorithms to perform the processing of an apparatus carrying out thetypes of techniques described herein. It should also be appreciatedthat, unless otherwise indicated herein, the sequence of steps and/oracts described in each flow chart is merely illustrative of thealgorithms that may be implemented and can be varied in implementationsand embodiments of the principles described herein.

Accordingly, in some embodiments, the techniques described herein may beembodied in computer-executable instructions implemented as software,including as application software, system software, firmware,middleware, embedded code, or any other suitable type of computer code.Such computer-executable instructions may be written using any ofseveral suitable programming languages and/or programming or scriptingtools and may be compiled as executable machine language code orintermediate code that is executed on a framework or virtual machine.

Computer-executable instructions implementing the techniques describedherein may, in some embodiments, be encoded on one or morecomputer-readable media to provide functionality to the media.Computer-readable media include magnetic media such as a hard diskdrive, optical media such as a Compact Disk (CD) or a Digital VersatileDisk (DVD), Blu-Ray disk, a persistent or non-persistent solid-statememory (e.g., Flash memory, Magnetic RAM, etc.), or any other suitablestorage media. As used herein, “computer-readable media” (also called“computer-readable storage media”) refers to tangible storage media.Tangible storage media are non-transitory and have at least onephysical, structural component. In a “computer-readable medium,” as usedherein, at least one physical, structural component has at least onephysical property that may be altered in some way during a process ofcreating the medium with embedded information, a process of recordinginformation thereon, or any other process of encoding the medium withinformation. For example, a magnetization state of a portion of aphysical structure of a computer-readable medium may be altered during arecording process.

While not illustrated in FIGS. 3A, 3B, 5A, and 5B, traffic analyticssystem 104 a, 104 b and intelligent telematics system 500 a, 500 b mayadditionally have one or more components and peripherals, includinginput and output devices. These devices can be used, among other things,to present a user interface. Examples of output devices that can be usedto provide a user interface include printers or display screens forvisual presentation of output and speakers or other sound generatingdevices for audible presentation of output. Examples of input devicesthat can be used for a user interface include keyboards, and pointingdevices, such as mice, touch pads, and digitizing tablets. As anotherexample, traffic analytics system 104 a, 104 b and intelligenttelematics system 500 a, 500 b may receive input information throughspeech recognition or in other audible format.

Embodiments have been described where the techniques are implemented incircuitry and/or computer-executable instructions. It should beappreciated that some embodiments may be in the form of a method orprocess, of which at least one example has been provided. The actsperformed as part of the method or process may be ordered in anysuitable way. Accordingly, embodiments may be constructed in which actsare performed in an order different than illustrated, which may includeperforming some acts simultaneously, even though shown as sequentialacts in illustrative embodiments. Various aspects of the embodimentsdescribed above may be used alone, in combination, or in a variety ofarrangements not specifically discussed in the embodiments described inthe foregoing and is therefore not limited in its application to thedetails and arrangement of components set forth in the foregoingdescription or illustrated in the drawings. For example, aspectsdescribed in one embodiment may be combined in any manner with aspectsdescribed in other embodiments.

Embodiments of the present invention provide one or more technicaleffects. In particular, the ability to repurpose raw vehicle dataindicative of vehicle operating conditions originally intended for fleetmanagement for use by a traffic analytics system and/or an intelligenttelematics system for defining road networks. Using speed data andignition state data of raw vehicle data for defining geographicalboundaries of road networks. Implementing machine learning techniquesusing raw vehicle data to define the location of road networks. Providesalternative techniques compared to prior art for locating road networks.Such as, image and video capture and processing, GIS measurementtechniques, gathering position data from targeted GPS devices, andgathering data uploaded data by the public. Ability to define locationsof road networks without obtaining location data from 3rd party,performing complex imagine processing or extracting road networklocation data from a 3rd party website. Once locations of vehicle waysare determined, the ability to obtain traffic data and/or trafficmetrics related to the vehicle way.

Nomenclature

Vehicle: a transportation asset, some examples include: a car, truck,recreational vehicle, heavy equipment, tractor, and snowmobile.

Vehicle way: an area frequently used by vehicles, i.e., an area on theEarth's surface repeatedly employed by vehicles. A vehicle way mayinclude an area employed by vehicles for movement and/or parking.

Location: a unique geographic location of an object on the Earth'ssurface.

Point location/Location of a point: defines a unique two-dimensionallocation of a point on the Earth's surface, for example, geographiccoordinate pair, latitude/longitude.

Area location/Location of an area: a unique two-dimensional space on theEarth's surface.

Known area: an area of which the location thereof is defined.

Monitoring device: a device onboard a vehicle for detectingenvironmental operating conditions associated with a vehicle andtransmitting raw vehicle data indicative thereof.

Raw vehicle data: data including vehicle operation informationindicative of vehicle operating conditions and the date and time vehicleoperating conditions were logged. Raw vehicle data may includeinformation for identifying an onboard monitoring device and/or avehicle the monitoring device is aboard.

Second historical vehicle data/Historical vehicle data: raw vehicle datacollected over a period of time.

Second vehicle data/Vehicle data: raw vehicle data and data interpolatedtherefrom or raw vehicle data.

Zone: an area encompassing an associated vehicle way.

Road Network Zone: an area encompassing a road network.

Subzone/Second subzone: portion of a zone/portion of a road networkzone.

Classifier: a classification model defined by using a machine learningtechnique for classifying an object. In context of this application, aclassifier classifies a subzone (e.g., a known area) as a vehicle wayclass or not-vehicle way class.

Feature: data indicative of variables/attributes, or measurements ofproperties of a phenomenon being observed and/or data derived therefrom.In context of this application, a feature is a numerical representationof a subzone.

What is claimed is:
 1. A traffic analytics system comprising a firstdatastore and a processing resource, the traffic analytics systemconfigured for: providing first road network data indicating a firstplurality of road network subzones defining a geographic area occupiedby a road network, the road network including a plurality ofintersections; processing first road network data for labelling eachroad network subzone as one of a core subzone and non-core subzone forforming a first plurality of core subzones and a first plurality ofnon-core subzones and storing an indication thereof in the first roadnetwork data; mapping each of the first plurality of core subzones to anintersection of the plurality of intersections of the road network andstoring an indication thereof in the first road network data; forming aplurality of subsets of core subzones of the first plurality of coresubzones, each thereof defining a geographic area occupied by anintersection core of an intersection of the plurality of intersections;processing first road network data for mapping each non-core subzone ofthe first plurality of non-core subzones to at least an intersection ofthe plurality of intersections; forming second road network dataincluding the first road network data and an indication of the at leastan intersection of the plurality of intersections of the road network towhich each non-core subzone of the first plurality of non-core subzonesis mapped; forming trip metadata dependent on second road network dataand third vehicle data corresponding to the first plurality of roadnetwork subzones; and processing trip metadata associated with a firstintersection of the intersection of the plurality of intersections forforming traffic metrics data indicative of intersection metrics for thefirst intersection.
 2. The traffic analytics system of claim 1 whereinprocessing first road network data for mapping each non-core subzone ofthe first plurality of non-core subzones to at least an intersection ofthe plurality of intersections of the road network includes, for eachroad network subzone of the first plurality of road network subzones,processing road network data for forming point data indicating a pointrepresenting a location of the road network subzone, for eachintersection of the plurality of intersections, processing point data ofa corresponding intersection core and point data of the first pluralityof non-core subzones for clustering corresponding points into groups,and for each point of a non-core subzone of the first plurality ofnon-core subzones grouped in a same group as points of an intersectioncore, mapping the non-core subzone to a corresponding intersection ofthe intersection core.
 3. The traffic analytics system of claim 2wherein processing point data of a corresponding intersection core andpoint data of the first plurality of non-core subzones for clusteringcorresponding points into groups includes, for at least a road networksubzone, processing road network data for determining a centre point ofthe road network subzone.
 4. The traffic analytics system of claim 2wherein processing point data of a corresponding intersection core andpoint data of the first plurality of non-core subzones for clusteringcorresponding points into groups includes clustering the correspondingpoints into groups using a spatial clustering algorithm.
 5. The trafficanalytics system of claim 4 wherein the spatial clustering algorithmincludes a density-based spatial clustering of applications with noisespatial clustering algorithm.
 6. The traffic analytics system of claim 1wherein forming trip metadata dependent on second road network data andthird vehicle data corresponding to the first plurality of road networksubzones includes, for each of a plurality of vehicles corresponding tothe third vehicle data, selecting at least a first subset of temporallyconsecutive third vehicle data instances indicating the vehicletransitions from a first undrivable state to a second drivable state toa third undrivable state for forming journey data; processing journeydata and road network data for mapping each instance of journey data toa road network subzone of the first plurality of road network subzonesbased on a journey data instance corresponding to a road network subzoneof the first plurality of road network subzones and storing anindication of a location of the road network subzone, a label of theroad network subzone, and intersection mapping of the road networksubzone in the journey data; selecting subsets of journey data instancesfor forming trip data indicative of vehicle trips and mapping the tripdata to an intersection of the plurality of intersections of the roadnetwork; and processing each trip data instance of the trip data forforming trip metadata.
 7. The traffic analytics system of claim 6wherein for a plurality of vehicles corresponding to the third vehicledata, selecting at least a first subset of temporally consecutive thirdvehicle data instances indicating the vehicle transitions from a firstundrivable state to a second drivable state to a third undrivable statefor forming journey data, includes, for at least a vehicle, selecting atleast a sequence of third vehicle data instances including a thirdvehicle data instance indicating ignition status of the vehicle is OFF,immediately followed by a third vehicle data instance indicating anignition status of the vehicle is ON and the vehicle has a speed greaterthan 0 km/h, immediately followed by one or more third vehicle datainstances indicating the vehicle is ON, immediately followed by a thirdvehicle data instance indicating ignition status of the vehicle is OFF.8. The traffic analytics system of claim 6 wherein selecting subsets ofjourney data instances for forming trip data indicative of vehicle tripsincludes, selecting at least a first sequence of journey data instancesfrom journey data for forming trip data, the at least a first sequenceof journey data instances including at least one journey data instancecorresponding to a core subzone that is mapped to a first intersectionimmediately followed by a journey data instance mapped to a secondintersection, wherein the second intersection and the first intersectionare not the same intersection; and mapping each trip data instance tothe first intersection and storing an indication of the mapping therein.9. The traffic analytics system of claim 6 wherein selecting subsets ofjourney data instances for forming trip data indicative of vehicle tripsincludes, selecting at least a first sequence of journey data instancesfrom journey data including at least one journey data instancecorresponding to a core subzone that is mapped to a first intersectionimmediately followed by a journey data instance mapped to a secondintersection, wherein the second intersection and the first intersectionare not the same intersection; selecting a second sequence of journeydata instances including at least a journey data instance correspondingto a non-core subzone mapped to the first intersection immediatelypreceding the at least a first sequence of journey data instances;forming trip data based on the first sequence of journey data instancesand the second sequence of journey data instances; and mapping each tripdata instance to the first intersection and storing an indication of themapping therein.
 10. The traffic analytics system of claim 6 whereinforming trip metadata includes forming trip metadata selected from agroup of: HardwareId data, VIN data, Make data, Model data, VehicleYeardata, WeightClass data, VehicleType data, Vocation data, TripID data,IntersectionId data, TimezoneName data, EventStartTimeUTC data,EventEndTimeUTC data, EventStartTimeLocal data, EventEndTimeLocal data,StartingLocation data, EntryCardinal data, ExitCardinal data,StreetNameEntry data, StreetNameExit data, SignalUsed data, TurnSignalsdata, TravelTime data, TravelDistance data, TravelSpeed data,RunningTime data, RunningSpeed data, MaxSpeed data, MinSpeed data,StopTimeTotal data, NumberOfStops data, TimeFromFirstStop data,DistanceFromFirstStop data, CoreDistance data, MaxAcceleration data, andMinAcceleration data.
 11. The traffic analytics system of claim 10wherein forming EntryCardinal data comprises determining a bearingbetween position data corresponding to a first instance of correspondingtrip data located in an intersection core and an immediately precedinginstance of corresponding journey data and creating EntryCardinal dataindicative of the bearing.
 12. The traffic analytics system of claim 10wherein forming ExitCardinal data includes, determining a bearingbetween position data corresponding to a last instance of correspondingtrip data located in an intersection core and an immediately followinginstance of corresponding journey data; and creating ExitCardinal dataindicative of the bearing.
 13. The traffic analytics system according toclaim 1 wherein processing trip metadata associated with the firstintersection for providing traffic metrics data indicative ofintersection metrics for the first intersection further includes,selecting a subset of trip metadata associated with the firstintersection for forming filtered trip metadata; and processing filteredtrip metadata for providing traffic metrics for the first intersection.14. The traffic analytics system of claim 13 wherein selecting a subsetof trip metadata for forming filtered trip metadata includes selecting asubset of trip metadata dependent on filter data.
 15. The trafficanalytics system of claim 14 wherein filter data is selected from agroup of: HardwareId data, VIN data, Make data, Model data, VehicleYeardata, WeightClass data, VehicleType data, Vocation data, IntersectionIddata, TimezoneName data, EventStartTimeUTC data, EventEndTimeUTC data,EventStartTimeLocal data, EventEndTimeLocal data, StartingLocation data,EntryCardinal data, ExitCardinal data, StreetNameEntry data,StreetNameExit data, SignalUsed data, TurnSignals data, TravelTime data,TravelDistance data, Travel Speed data, RunningTime data, RunningSpeeddata, MaxSpeed data, MinSpeed data, StopTimeTotal data, NumberOfStopsdata, TimeFromFirstStop data, DistanceFromFirstStop data, CoreDistancedata, MaxAcceleration data, and MinAcceleration data.
 16. The trafficanalytics system of claim 12 wherein filter data indicates one or moredays of a week.
 17. The traffic analytics system of claim 14 whereinfilter data includes filter data provided by a user.
 18. The trafficanalytics system of claim 1 wherein forming intersection metrics dataincludes forming intersection metrics data selected from a group of:PercentageStopping, AvgTravelSpeed, AvgRunningSpeed,AvgTotalTimeStopped, AvgTotalTimeStoppedNoZero, AvgTravelTime,AvgTimeFromFirstStop, AvgNumberOfStops, AvgNumberOfStopsNoZero,AvgDistanceFromIntersectionToFirstStop, PercentOfVolumeByVehicleClass,and NumberOfTrips.
 19. The traffic analytics system of claim 10 whereinforming trip metadata StreetNameEntry data, for at least an intersectionof the plurality of intersections, includes, receiving data indicativeof roads and location thereof within the area occupied by core subzonesof a corresponding intersection core; mapping each core subzone of theintersection core to a nearest road; and generating StreetNameEntry dataindicating a name of a road associated with a first core subzone of atrip.
 20. The traffic analytics system of claim 10 wherein forming tripmetadata StreetNameEntry data, for at least an intersection of theplurality of intersections, includes, receiving data indicative of roadsand location thereof within the area occupied by core subzones of acorresponding intersection core; mapping each core subzone of theintersection core to a nearest road; and generating StreetNameEntry dataindicating a name of a road associated with a last core subzone of atrip.